CN106446556A - Prediction method and prediction system for epifolliculitis based on incremental type neural network model - Google Patents
Prediction method and prediction system for epifolliculitis based on incremental type neural network model Download PDFInfo
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- 238000003062 neural network model Methods 0.000 title claims abstract description 80
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- 238000007689 inspection Methods 0.000 claims abstract description 24
- 238000012937 correction Methods 0.000 claims abstract description 8
- 206010016936 Folliculitis Diseases 0.000 claims description 126
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- 238000012544 monitoring process Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 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
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- 210000003780 hair follicle Anatomy 0.000 claims 1
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Abstract
The invention discloses a prediction method for epifolliculitis based on an incremental type neural network model. The prediction method comprises the steps of establishing a daily data database of epifolliculitis; conducting training on the neural network model; collecting daily life data and sending to a server, and storing the daily life data in a user daily data record sheet; extracting the data of the day from the user daily data record sheet to form a n-dimension vector quantity, inputting the n-dimension vector quantity into an epifolliculitis pathology neural network model to conduct epifolliculitis probability forecasting after the n-dimension vector quantity is subjected to normalization processing; using intelligent home epifolliculitis nurse equipment to judge whether a epifolliculitis probability value is bigger than 0.5; going to the hospital by oneself to inspect when the user is judged to have the epifolliculitis, and transferring an inspection result through the intelligent home epifolliculitis nurse equipment back to the server which judges whether the inspection result is correct; executing an incremental type algorithm when the inspection result is incorrect, and conducting a dynamic correction on the neural network model. The prediction method for epifolliculitis based on the incremental type neural network model is accurate in prediction, and a tailor made neural network model is made for every user.
Description
Technical field
The invention belongs to field of medical technology, more particularly to a kind of folliculitises based on increment type neural network model are pre-
Survey method and prognoses system.
Background technology
Currently domestic each health management system arranged be respectively provided with folliculitises 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 folliculitises so current.
Most of before this is all using BP neural network model to folliculitises 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
Folliculitises Forecasting Methodology and prognoses system, the present invention by neural network model train predict a large amount of patient in hospital pathology numbers
According to, find folliculitises pathology and folliculitises earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this
Logic association between several causes of disease and variable, ultimately form the folliculitises pathology nerve to folliculitises 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
Folliculitises pathology Neural Network model predictive user suffers from folliculitises 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 folliculitises prediction side based on increment type neural network model
Method, comprises the steps：
Step (1), acquisition hospital's folliculitises etiology and pathology data source and the daily monitoring data of patient, thus set up folliculitises
Daily data database；
Step (2), the daily data database of folliculitises set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the folliculitises 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 folliculitises probabilistic forecasting in the folliculitises pathology neural network model training in input step (2) after normalized,
Obtain folliculitises probability, server sends folliculitises probability to wired home folliculitises care appliances；
After step (5), the folliculitises probability of wired home folliculitises care appliances the reception server transmission, judge folliculitises
Whether probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained folliculitises, attention device warns to remind user,
If less than 0.5, then it is judged to that this user does not obtain folliculitises；
Step (6), when user is judged to folliculitises, user voluntarily removes examination in hospital, and inspection result is passed through
Wired home folliculitises care appliances send back server, and server judges whether inspection result is correct, if inspection result is wrong
By mistake, then explanation folliculitises pathology Neural Network model predictive is inaccurate, if inspection result is correct, folliculitises pathology god is described
Through network model's 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 folliculitises 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 folliculitises 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 iterationses 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 folliculitises 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 folliculitises 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
Meansigma methodss 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 folliculitises, and intelligence
The attention device of family's folliculitises care appliances do not warn then it represents that wired home folliculitises care appliances judge inaccurate, this
When execution step (6)～(7), wired home folliculitises care appliances are sent to object information on server.
Present invention also offers a kind of prognoses system of described folliculitises Forecasting Methodology, including intelligent monitoring device, intelligence
Device data acquisition device, server and wired home folliculitises 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 folliculitises care appliances and pass through communication device two and described server network communication.
Further, described wired home folliculitises 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 folliculitises pathology with
Folliculitises earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic between this several causes of disease
Association and variable, ultimately form the folliculitises pathology neural network model to folliculitises 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 folliculitises pathology nerve net
Network model prediction user suffers from folliculitises 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 to embodiment or
In description of the prior art the accompanying drawing of required use be briefly described it should be apparent that, drawings in the following description are only
Some embodiments of the present invention, for those of ordinary skill in the art, on the premise of not paying creative work, also may be used
So that 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 folliculitises Forecasting Methodology based on increment type neural network model that the present invention provides, including
Following steps：
Step (1), acquisition hospital's folliculitises etiology and pathology data source and the daily monitoring data of patient, thus set up folliculitises
Daily data database；
Wherein daily monitoring data is 21 item data, and its 21 item data is the age, sex, the course of disease time, recurrence frequency, body
Dry skin situation, erythra image, domestic environment, the erythra order of severity, sufferings degree, time for falling asleep, smoking capacity (daily), extremity
Skin conditions, daily travel distance, pursue an occupation, temperature, humidity, air quality index etc. 21 item data, the present invention is with 21
Data sets up 21 dimensional vectors；
Step (2), the daily data database of folliculitises set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the folliculitises 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 folliculitises probability pre- in the folliculitises pathology neural network model training in input step (2) after doing normalized
Survey, obtain folliculitises probability, server sends folliculitises probability to wired home folliculitises care appliances；
After step (5), the folliculitises probability of wired home folliculitises care appliances the reception server transmission, judge folliculitises
Whether probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained folliculitises, attention device warns to remind user,
If less than 0.5, then it is judged to that this user does not obtain folliculitises；
Step (6), when user is judged to folliculitises, user voluntarily removes examination in hospital, and inspection result is passed through
Wired home folliculitises care appliances send back server, and server judges whether inspection result is correct, if inspection result is wrong
By mistake, then explanation folliculitises pathology Neural Network model predictive is inaccurate, if inspection result is correct, folliculitises pathology god is described
Through network model's 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 folliculitises 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 folliculitises), extracts 400000 records from folliculitises 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 iterationses 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 folliculitises 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 folliculitises 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
Meansigma methodss 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 folliculitises, and intelligence
The attention device of family's folliculitises care appliances do not warn then it represents that wired home folliculitises care appliances judge inaccurate, this
When execution step (6)～(7), wired home folliculitises care appliances are sent to object information on server.
Present invention also offers a kind of prognoses system of described folliculitises Forecasting Methodology, including intelligent monitoring device, intelligence
Device data acquisition device, server and wired home folliculitises 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 folliculitises care appliances and pass through communication device two and described server network communication.
It is provided with attention device on the described wired home folliculitises 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 folliculitises pathology and hair
Capsulitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic between this several causes of disease is closed
Connection and variable, ultimately form the folliculitises pathology neural network model to folliculitises 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 folliculitises pathology neutral net
Model prediction user suffers from folliculitises 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 characteristicses being shown during folliculitises morbidity also can be different.Cause
This is not conventional high by the method accuracy rate of neural network prediction folliculitises.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 folliculitises 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 folliculitises care appliances of the present invention will be more and more accurate.
Ultimate 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 description 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 folliculitises Forecasting Methodology based on increment type neural network model is it is characterised in that comprise the steps：
Step (1), obtain hospital folliculitises and cure the disease etiology and pathology data source and the daily monitoring data of patient, thus setting up folliculitises
Daily data database；
Step (2), off-line manner neural network model is entered according to the daily data database of folliculitises that step (1) is set up
Row training, to obtain the folliculitises 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 folliculitises probabilistic forecasting in the folliculitises pathology neural network model training in input step (2) after change process, obtain
Folliculitises probability, server sends folliculitises probability to wired home folliculitises care appliances；
After step (5), the folliculitises probability of wired home folliculitises care appliances the reception server transmission, judge folliculitises probability
Whether value is more than 0.5, if greater than 0.5, is then judged to that this user obtained folliculitises, attention device warns to remind user, if
Less than 0.5, then it is judged to that this user does not obtain folliculitises；
Step (6), when user is judged to folliculitises, user voluntarily removes examination in hospital, and by inspection result pass through intelligence
Family's folliculitises care appliances send back server, and server judges whether inspection result is correct, if inspection result mistake,
Illustrate that folliculitises pathology Neural Network model predictive is inaccurate, if inspection result is correct, folliculitises 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 folliculitises pathology nerve net
Network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
2. a kind of folliculitises 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 hair follicle buring sun
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 iterationses 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 folliculitises 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 folliculitises 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 folliculitises 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 folliculitises 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 folliculitises 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 folliculitises, and the nursing of wired home folliculitises
The attention device of equipment do not warn then it represents that wired home folliculitises care appliances judge inaccurate, now execution step (6)～
(7), wired home folliculitises care appliances are sent to object information on server.
6. a kind of prognoses system of folliculitises 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 folliculitises 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 folliculitises care appliances pass through communication device two and described server network communication.
7. according to claim 7 the prognoses system of folliculitises Forecasting Methodology it is characterised in that described wired home folliculitises
Attention device is provided with care appliances.
8. according to claim 7 the prognoses system of folliculitises 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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