CN106446561A - Incremental neural network model based urticaria prediction method and system - Google Patents
Incremental neural network model based urticaria prediction method and system Download PDFInfo
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- 208000024780 Urticaria Diseases 0.000 title claims abstract description 136
- 238000003062 neural network model Methods 0.000 title claims abstract description 79
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- 230000007170 pathology Effects 0.000 claims abstract description 32
- 238000012549 training Methods 0.000 claims abstract description 25
- 238000012937 correction Methods 0.000 claims abstract description 8
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- 210000002569 neuron Anatomy 0.000 claims description 111
- 238000007689 inspection Methods 0.000 claims description 21
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- 230000036541 health Effects 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 5
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- 210000005036 nerve Anatomy 0.000 claims description 4
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 claims description 3
- 241000190070 Sarracenia purpurea Species 0.000 claims description 3
- 230000033228 biological regulation Effects 0.000 claims description 3
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- 210000004218 nerve net Anatomy 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims description 3
- 238000011017 operating method Methods 0.000 claims description 3
- 241000894007 species Species 0.000 claims description 3
- 238000010606 normalization Methods 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 4
- 230000007935 neutral effect Effects 0.000 description 4
- 208000024891 symptom Diseases 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 230000001575 pathological effect Effects 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 208000004998 Abdominal Pain Diseases 0.000 description 1
- 201000004569 Blindness Diseases 0.000 description 1
- 206010012735 Diarrhoea Diseases 0.000 description 1
- 206010019233 Headaches Diseases 0.000 description 1
- 201000005505 Measles Diseases 0.000 description 1
- 206010028813 Nausea Diseases 0.000 description 1
- 241000219422 Urtica Species 0.000 description 1
- 235000009108 Urtica dioica Nutrition 0.000 description 1
- 206010047700 Vomiting Diseases 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The invention discloses an incremental neural network model based urticaria prediction method. The method includes the following steps: setting up a daily urticaria data database; training a neural network model; acquiring daily life data prior to sending to a server, and storing the daily life data into a daily user data log sheet; extracting current day data from the daily user data log sheet, forming n-dimensional vectors, and performing normalization processing prior to inputting the data into a urticaria pathology neural network model for urticaria probability prediction; smart household urticaria nursing equipment judging whether a urticaria probability value is larger than 0.5 or not; when a user is judged to have urticaria, the user going to hospital for examination by himself, transmitting examination results to the server through the smart household urticaria nursing equipment, and the server judging whether the examination results are accurate or not; when the examination results are wrong, executing an incremental algorithm, and subjecting the neural network model to dynamic correction. Predication is accurate, and the neural network model is customized for each user.
Description
Technical field
The invention belongs to field of medical technology, more particularly to a kind of nettle rash based on increment type neural network model is pre-
Survey method and forecasting system.
Background technology
Currently domestic each health management system arranged be respectively provided with nettle rash prediction and evaluation, its use prediction mode be data
Join.Its principle is that by system matches fixed data and then personal lifestyle data entry system is shown ill probability.But due to people
The complexity of body and disease, unpredictability, in the form of expression of bio signal and information, Changing Pattern (Self-variation with
Change after medical intervention) on, it is detected and signal representation, the data of acquisition and the analysis of information, decision-making etc. are all multi-party
All there is extremely complex non-linear relationship in face.So using traditional Data Matching can only be blindness data examination it is impossible to
Judge the logic association between data and data and variable, the codomain deviation obtaining is big, causes the specificity ten of system prediction
Point poor, domestic health management system arranged effectively Accurate Prediction cannot be carried out to personal nettle rash so current.
Most of before this is all using BP neural network model to nettle rash prediction, but when new detection data generation
When it is necessary to train neural network model again, operation efficiency is extremely low.And after system user scale increases, server
Will be unable to complete in time training mission.
Content of the invention
The purpose of the present invention is that and overcomes the deficiencies in the prior art, there is provided one kind is based on increment type neural network model
Nettle rash Forecasting Methodology and forecasting system, the present invention by neural network model train predict a large amount of patient in hospital pathology numbers
According to, find nettle rash pathology and nettle rash earlier life variations in detail, clinical symptoms, examination criteria value, people at highest risk's feature, this
Logic association between several causes of disease and variable, ultimately form the nettle rash pathology nerve to nettle rash illness probability Accurate Prediction
Network model, the present invention passes through to gather user's daily life data, the periodicity of its data of active analysis, regular eventually through
Nettle rash pathology Neural Network model predictive user suffers from nettle rash probability, immediately reminds user in the way of visual effect
Doctor, constantly revises neural network model when Neural Network model predictive is inaccurate by increasable algorithm, to set for each
Standby user sets up the neural network model training for this user, with the increase of use time, to set up to this customer volume
Body neural network model customized, accuracy rate is greatly improved.
To achieve these goals, the invention provides a kind of nettle rash prediction side based on increment type neural network model
Method, comprises the steps：
Step (1), acquisition hospital's nettle rash etiology and pathology data source and the daily monitoring data of patient, thus set up nettle rash
Daily data database；
Step (2), the daily data database of nettle rash set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the nettle rash pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life
Live data sends to server, and server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form n-dimensional vector, and n-dimensional vector is done
Carry out nettle rash probabilistic forecasting in the nettle rash pathology neural network model training in input step (2) after normalized,
Obtain nettle rash probability, server sends nettle rash probability to wired home nettle rash care appliances；
After step (5), the nettle rash probability of wired home nettle rash care appliances the reception server transmission, judge nettle rash
Whether probable value is more than 0.5, if greater than 0.5, is then judged to that this user obtained nettle rash, attention device warns to remind user,
If less than 0.5, then it is judged to that this user does not obtain nettle rash；
Step (6), when user is judged to nettle rash, user voluntarily removes examination in hospital, and inspection result is passed through
Wired home nettle rash care appliances send back server, and server judges whether inspection result is correct, if inspection result is wrong
By mistake, then explanation nettle rash pathology Neural Network model predictive is inaccurate, if inspection result is correct, nettle rash pathology god is described
Through network model prediction accurately；
Step (7), when inspection result mistake, from the daily data logger of user extract m days in record preserve to
In incremental data table, when the record quantity in incremental data table is more than h bar, execute increasable algorithm, to nettle rash pathology god
Carry out dynamic corrections through network model；
Step (8), repeat step (3)～(7).
Further, the input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1
Node, extracts k bar record from nettle rash daily data database table and is trained, every record is a n-dimensional vector, owns
Data before use first through normalized so as to numerical value is interval in [0,1], then execution following steps are to neural network model
It is trained：
1) one n-dimensional vector of input, to neural network model, calculates all of weight vector in neural network model defeated to this
Enter the distance of n-dimensional vector, closest neuron is as won neuron, its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Domain；α (t) is learning rate, and it is as the function that the increase of iterations is gradually successively decreased, and span is [0 1], through multiple
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases
Number；Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
Further, inspection result is sent back the form of the object information of server by wired home nettle rash care appliances
For：{ checking whether correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
Further, the increasable algorithm carrying out dynamic corrections to nettle rash pathology neural network model is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function
Row study, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes input mode vector V
Mean value Avg (V), be initialized as the weights of unique neuron in the 0th layer of neural network model, and be set to win nerve
Unit, calculates its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, initialize the power of this 4 neurons
Value；The input vector set Ci of i-th neuron is set to sky, main label is set to NULL, the main label ratio r of neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance is the shortest
Neuron is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is made
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ that wins
{VXi, calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training,
Go to step 4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, turn step
Rapid 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will
The subnet newly growing increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) to it again
It is trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
Further, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from nettle rash, and intelligence
The attention device of family's nettle rash care appliances do not warn then it represents that wired home nettle rash care appliances judge inaccurate, this
When execution step (6)～(7), wired home nettle rash care appliances are sent to object information on server.
Present invention also offers a kind of forecasting system of described nettle rash Forecasting Methodology, including intelligent monitoring device, intelligence
Device data acquisition device, server and wired home nettle rash care appliances, described intelligent monitoring device and described smart machine
Data acquisition unit is connected, and described smart machine data acquisition unit passes through communication device one and described server network communication, institute
State wired home nettle rash care appliances and pass through communication device two and described server network communication.
Further, described wired home nettle rash care appliances are provided with attention device.
Further, described intelligent monitoring device includes Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent horse
Bucket and Intelligent light sensing equipment.
Beneficial effects of the present invention：
1st, the present invention by neural network model train predict a large amount of patient in hospital pathological data, find nettle rash pathology with
Nettle rash earlier life variations in detail, clinical symptoms, examination criteria value, people at highest risk's feature, the logic between this several causes of disease
Association and variable, ultimately form the nettle rash pathology neural network model to nettle rash illness probability Accurate Prediction, and the present invention is led to
Cross collection user's daily life data, the periodicity of its data of active analysis, regularity are eventually through nettle rash pathology nerve net
Network model prediction user suffers from nettle rash probability, reminds user's instant hospitalizing and prevention in the way of visual effect.
2nd, when Neural Network model predictive is inaccurate, neural network model is constantly revised, to be directed to by increasable algorithm
Each equipment user sets up the neural network model training for this user, with the increase of use time, to set up to this
The neural network model that user makes to measure, accuracy rate is greatly improved.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, acceptable
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings invention is further illustrated, but be not limited to the scope of the present invention.
Embodiment
As shown in figure 1, a kind of nettle rash Forecasting Methodology based on increment type neural network model that the present invention provides, including
Following steps：
Step (1), acquisition hospital's nettle rash etiology and pathology data source and the daily monitoring data of patient, thus set up nettle rash
Daily data database；
Wherein daily monitoring data is 21 item data, and its 21 item data is the age, sex, blood pressure, breathing, body skin feelings
Condition, face, headache situation, the fash order of severity, diarrhoea, degree of suffering from abdominal pain, the nauseous situation of vomiting, fash image (daily), four limbs
Skin conditions, articular conditions, pursue an occupation, temperature, humidity, air quality index etc. 21 item data, the present invention is with 21 item data
Set up 21 dimensional vectors；
Step (2), the daily data database of nettle rash set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the nettle rash pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life
Live data sends to server, and server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form 21 dimensional vectors, and to 21 dimensional vectors
Carry out nettle rash probability pre- in the nettle rash pathology neural network model training in input step (2) after doing normalized
Survey, obtain nettle rash probability, server sends nettle rash probability to wired home nettle rash care appliances；
After step (5), the nettle rash probability of wired home nettle rash care appliances the reception server transmission, judge nettle rash
Whether probable value is more than 0.5, if greater than 0.5, is then judged to that this user obtained nettle rash, attention device warns to remind user,
If less than 0.5, then it is judged to that this user does not obtain nettle rash；
Step (6), when user is judged to nettle rash, user voluntarily removes examination in hospital, and inspection result is passed through
Wired home nettle rash care appliances send back server, and server judges whether inspection result is correct, if inspection result is wrong
By mistake, then explanation nettle rash pathology Neural Network model predictive is inaccurate, if inspection result is correct, nettle rash pathology god is described
Through network model prediction accurately；
Step (7), when inspection result mistake, from the daily data logger of user extract 7 days in record preserve to
In incremental data table, when the record quantity in incremental data table is more than 100, execute increasable algorithm, to nettle rash pathology
Neural network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
The input layer of the neural network model of the present invention is 21 nodes, and hidden layer number is 43, and output layer is 1 node
(i.e. the probability of nettle rash), extracts 400000 records from nettle rash daily data database table and is trained, every record
21 dimensional vectors, all data before use first through normalized so as to numerical value is interval in [0,1], then execute such as
Lower step is trained to neural network model：
1) one 21 dimensional vector of input, to neural network model, calculate all of weight vector in neural network model defeated to this
Enter the distance of 21 dimensional vectors, closest neuron is as won neuron, its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Domain；α (t) is learning rate, and it is as the function that the increase of iterations is gradually successively decreased, and span is [0 1], through multiple
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases
Number；Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
Inspection result is sent back the form of the object information of server by the wired home nettle rash care appliances of the present invention
For：{ checking whether correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
The increasable algorithm carrying out dynamic corrections to nettle rash pathology neural network model of the present invention is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function
Row study, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes input mode vector V
Mean value Avg (V), be initialized as the weights of unique neuron in the 0th layer of neural network model, and be set to win nerve
Unit, calculates its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, initialize the power of this 4 neurons
Value；The input vector set Ci of i-th neuron is set to sky, main label is set to NULL, the main label ratio r of neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance is the shortest
Neuron is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is made
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ that wins
{VXi, calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training,
Go to step 4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, turn step
Rapid 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will
The subnet newly growing increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) to it again
It is trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
If the user of the present invention includes health check-up by other means and checks oneself, learn that oneself has suffered from nettle rash, and intelligence
The attention device of family's nettle rash care appliances do not warn then it represents that wired home nettle rash care appliances judge inaccurate, this
When execution step (6)～(7), wired home nettle rash care appliances are sent to object information on server.
Present invention also offers a kind of forecasting system of described nettle rash Forecasting Methodology, including intelligent monitoring device, intelligence
Device data acquisition device, server and wired home nettle rash care appliances, described intelligent monitoring device and described smart machine
Data acquisition unit is connected, and described smart machine data acquisition unit passes through communication device one and described server network communication, institute
State wired home nettle rash care appliances and pass through communication device two and described server network communication.
It is provided with attention device on the described wired home nettle rash care appliances of the present invention.
The described intelligent monitoring device of the present invention includes Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool
With Intelligent light sensing equipment etc..
The present invention is trained by neural network model and predicts a large amount of patient in hospital pathological data, finds nettle rash pathology and nettle
Measles earlier life variations in detail, clinical symptoms, examination criteria value, people at highest risk's feature, the logic between this several causes of disease is closed
Connection and variable, ultimately form the nettle rash pathology neural network model to nettle rash illness probability Accurate Prediction, and the present invention passes through
Collection user's daily life data, the periodicity of its data of active analysis, regularity are eventually through nettle rash pathology neutral net
Model prediction user suffers from nettle rash probability, reminds user's instant hospitalizing and prevention in the way of visual effect.
All data of the present invention preserve to server, can significantly save calculating cost, hardware configuration is low, thus selling
Valency is also low.
The present invention carries communication device one and communication device two, by wifi from the internet that is dynamically connected, and can protect for a long time
Hold online.Various intelligent monitoring devices can easily access present device by modes such as network or bluetooths, sets in acquisition
The daily life data of the monitoring of intelligent monitoring device, the data that therefore present device obtains can automatically be uploaded after standby mandate
It is real-time, accurate, polynary.
Because everyone physical trait is different, the data characteristics being shown during nettle rash morbidity also can be different.Cause
This is not conventional high by the method accuracy rate of neural network prediction nettle rash.The present invention is directed to each equipment user and sets up training
Go out the neural network model for this user, running after a period of time, by producing to measure neutral net is being made to this user
Forecast model, accuracy rate is greatly improved.
When neural network model is judged by accident, error message be will be feedbacked to server by wired home nettle rash care appliances,
For this user's dynamic corrections neural network model, when similar characteristics data in this user next, will not judge by accident again.Cause
This, with the increase of use time, the judgement of the wired home nettle rash care appliances of the present invention will be more and more accurate.
General principle, principal character and the advantages of the present invention of the present invention have been shown and described above.The technology of the industry
, it should be appreciated that the present invention is not restricted to the described embodiments, the simply explanation described in above-described embodiment and specification is originally for personnel
Invention principle, without departing from the spirit and scope of the present invention the present invention also have various changes and modifications, these change
Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and its
Equivalent defines.
Claims (8)
1. a kind of nettle rash Forecasting Methodology based on increment type neural network model is it is characterised in that comprise the steps：
Step (1), obtain hospital nettle rash and cure the disease etiology and pathology data source and the daily monitoring data of patient, thus setting up nettle rash
Daily data database；
Step (2), off-line manner neural network model is entered according to the daily data database of nettle rash that step (1) is set up
Row training, to obtain the nettle rash pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life number
According to sending to server, server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form n-dimensional vector, and normalizing is done to n-dimensional vector
Carry out nettle rash probabilistic forecasting in the nettle rash pathology neural network model training in input step (2) after change process, obtain
Nettle rash probability, server sends nettle rash probability to wired home nettle rash care appliances；
After step (5), the nettle rash probability of wired home nettle rash care appliances the reception server transmission, judge nettle rash probability
Whether value is more than 0.5, if greater than 0.5, is then judged to that this user obtained nettle rash, attention device warns to remind user, if
Less than 0.5, then it is judged to that this user does not obtain nettle rash；
Step (6), when user is judged to nettle rash, user voluntarily removes examination in hospital, and by inspection result pass through intelligence
Family's nettle rash care appliances send back server, and server judges whether inspection result is correct, if inspection result mistake,
Illustrate that nettle rash pathology Neural Network model predictive is inaccurate, if inspection result is correct, nettle rash pathology nerve net is described
Network model prediction is accurate；
Step (7), when inspection result mistake, from the daily data logger of user extract m days in record preserve to increment
In tables of data, when the record quantity in incremental data table is more than h bar, execute increasable algorithm, to nettle rash pathology nerve net
Network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
2. a kind of nettle rash Forecasting Methodology based on increment type neural network model according to claim 1, its feature exists
In the input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1 node, from nettle rash day
Extract k bar record in regular data database table to be trained, every record is a n-dimensional vector, all data are first before use
Through normalized so as to numerical value is interval in [0,1], then execution following steps are trained to neural network model：
1) one n-dimensional vector of input, to neural network model, calculates all of weight vector in neural network model and ties up to this input n
The distance of vector, closest neuron is as won neuron, and its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron field；α
T () is learning rate, it is as the function that the increase of iterations is gradually successively decreased, and span is [0 1], through many experiments
Choosing Optimal learning efficiency is 0.62；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the function that the time successively decreases；
Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration time reaching regulation
After number, neural network model training terminates.
3. a kind of nettle rash Forecasting Methodology based on increment type neural network model according to claim 1, its feature exists
In the form that inspection result is sent back the object information of server by wired home nettle rash care appliances is：{ just check whether
Really, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
4. a kind of nettle rash Forecasting Methodology based on increment type neural network model according to claim 1, its feature exists
In the increasable algorithm carrying out dynamic corrections to nettle rash pathology neural network model is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function and learned
Practise, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes the flat of input mode vector V
Average Avg (V), is initialized as the weights of unique neuron in the 0th layer of neural network model, and is set to triumph neuron, meter
Calculate its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the weights of this 4 neurons are initialized；Will
The input vector set Ci of i-th neuron is set to sky, and main label is set to NULL, the main label ratio r of neuron iiIt is set to
0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance nerve the shortest
Unit is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is as obtaining
Victory neuron, updates this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i is as obtaining
Victory neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ { VX that winsi,
Calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training, go to step
4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy EiAnd son
The average quantization error MQE of net, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall the total of label data on neuron
Number, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, go to step 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will be new
The subnet growing increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) it is re-started
Training, until neural network model no longer produces new neuron and new layering, whole training terminates.
5. a kind of nettle rash Forecasting Methodology based on increment type neural network model according to claim 1, its feature exists
In, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from nettle rash, and the nursing of wired home nettle rash
The attention device of equipment do not warn then it represents that wired home nettle rash care appliances judge inaccurate, now execution step (6)～
(7), wired home nettle rash care appliances are sent to object information on server.
6. a kind of forecasting system of nettle rash Forecasting Methodology described in employing claim 1～6 is supervised it is characterised in that including intelligence
Control equipment, smart machine data acquisition unit, server and wired home nettle rash care appliances, described intelligent monitoring device and institute
State smart machine data acquisition unit to be connected, described smart machine data acquisition unit passes through communication device one and described server net
Network communicates, and described wired home nettle rash care appliances pass through communication device two and described server network communication.
7. according to claim 7 the forecasting system of nettle rash Forecasting Methodology it is characterised in that described wired home nettle rash
Attention device is provided with care appliances.
8. according to claim 7 the forecasting system of nettle rash Forecasting Methodology it is characterised in that described intelligent monitoring device bag
Include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool and Intelligent light sensing equipment.
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