CN106446554A - Seborrheic dermatitis prediction method and prediction system based on incremental neural network model - Google Patents
Seborrheic dermatitis prediction method and prediction system based on incremental neural network model Download PDFInfo
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- 208000008742 seborrheic dermatitis Diseases 0.000 title claims abstract description 129
- 206010039793 Seborrhoeic dermatitis Diseases 0.000 title claims abstract description 127
- 238000003062 neural network model Methods 0.000 title claims abstract description 82
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- 210000002569 neuron Anatomy 0.000 claims description 111
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- 201000004624 Dermatitis Diseases 0.000 claims description 10
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Abstract
The invention discloses a seborrheic dermatitis prediction method based on an incremental neural network model. The seborrheic dermatitis prediction method comprises the following steps: establishing a database of daily data of gastric concretion; training a neural network model; acquiring daily living data and transmitting to a server; extracting data of a day from a daily data record table of a user, forming an n-dimensional vector, performing normalization processing, inputting into a seborrheic dermatitis pathology neural network model, and performing seborrheic dermatitis criticality probability prediction; determining whether a seborrheic dermatitis criticality value is greater than 0.5 or not by using intelligent domestic seborrheic dermatitis nursing equipment; when determining that the user has seborrheic dermatitis, reminding the user to take inspection in a hospital, transmitting an inspection result to the server through the intelligent domestic seborrheic dermatitis nursing equipment, and determining whether the inspection result is correct or not by the server; when the inspection result is wrong, implementing an incremental algorithm, and performing dynamic modification on the neural network model. The seborrheic dermatitis prediction method is accurate in prediction, and the neural network model can be customized for each user.
Description
Technical field
The invention belongs to field of medical technology, more particularly to a kind of seborrheica skin based on increment type neural network model
Scorching Forecasting Methodology and prognoses system.
Background technology
Currently domestic each health management system arranged be respectively provided with seborrheic dermatitis prediction and evaluation, its use prediction mode be data
Coupling.Its principle is that by system matches fixed data and then personal lifestyle data entry system is shown ill probability.But due to
The complexity of human body and disease, unpredictability, in the form of expression with information for the bio signal, Changing Pattern (Self-variation
Change with after medical intervention) on, it is detected and signal representation, the data of acquisition and the analysis of information, decision-making etc. are many
All there is extremely complex non-linear relationship in aspect.So can only be the data examination of blindness using traditional Data Matching, no
Method judges the logic association and variable between data and data, and the codomain deviation obtaining is big, causes the specificity of system prediction
Very poor, domestic health management system arranged effectively Accurate Prediction cannot be carried out to personal seborrheic dermatitis so current.
Most of before this is all using BP neural network model to seborrheic dermatitis prediction, but when new detection data is produced
It is necessary to train neural network model again when raw, operation efficiency is extremely low.And after system user scale increases, clothes
Business device 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
Seborrheic dermatitis Forecasting Methodology and prognoses system, the present invention by neural network model train predict a large amount of patient in hospital pathology
Data, finds seborrheic dermatitis pathology and seborrheic dermatitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk
Crowd characteristic, the logic association between this several causes of disease and variable, ultimately form to seborrheic dermatitis illness probability Accurate Prediction
Seborrheic dermatitis pathology neural network model, the present invention pass through gather user's daily life data, its data of active analysis
Periodically, regular suffer from seborrheic dermatitis probability eventually through seborrheic dermatitis pathology Neural Network model predictive user, with
The mode of visual effect reminds user's instant hospitalizing, is constantly repaiied by increasable algorithm when Neural Network model predictive is inaccurate
Positive neural network model, to set up, for each equipment user, the neural network model training for this user, with use
The increase of time, to set up the neural network model that this user is made to measure, accuracy rate is greatly improved.
To achieve these goals, the invention provides a kind of seborrheic dermatitis based on increment type neural network model are pre-
Survey method, comprises the steps：
Step (1), acquisition hospital's seborrheic dermatitis etiology and pathology data source and the daily monitoring data of patient, thus set up fat
The daily data database of excessive property dermatitis；
Step (2), the daily data database of seborrheic dermatitis set up according to step (1) are off-line manner to nerve net
Network model is trained, to obtain the seborrheic dermatitis 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 seborrheic dermatitis general in the seborrheic dermatitis pathology neural network model training in input step (2) after normalized
Rate is predicted, obtains seborrheic dermatitis probability, and server sends seborrheic dermatitis probability the nursing of to wired home seborrheic dermatitis
Equipment；
After step (5), the seborrheic dermatitis probability of wired home seborrheic dermatitis care appliances the reception server transmission, sentence
Whether disconnected seborrheic dermatitis probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained seborrheic dermatitis, attention device
Warning, to remind user, if less than 0.5, is then judged to that this user does not obtain seborrheic dermatitis；
Step (6), when user is judged to seborrheic dermatitis, user voluntarily removes examination in hospital, and by inspection result
Send back server by wired home seborrheic dermatitis care appliances, server judges whether inspection result is correct, if inspection
Come to an end fruit mistake, then explanation seborrheic dermatitis pathology Neural Network model predictive is inaccurate, if inspection result is correct, illustrates
Seborrheic dermatitis pathology Neural Network model predictive is accurate；
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 seborrheic dermatitis disease
Reason neural network model carries out dynamic corrections；
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 seborrheic dermatitis daily data database table and is trained, every record is a n-dimensional vector,
All data before use first through normalized so as to numerical value is interval in [0,1], then execution following steps are to nerve net
Network model 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 object information of server by wired home seborrheic dermatitis care appliances
Form is：{ 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 seborrheic dermatitis 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 seborrheic dermatitis, and
The attention device of wired home seborrheic dermatitis care appliances does not warn then it represents that wired home seborrheic dermatitis care appliances are sentenced
Disconnected inaccurate, now execution step (6)～(7), wired home seborrheic dermatitis care appliances are sent to service object information
On device.
Present invention also offers a kind of prognoses system of described seborrheic dermatitis Forecasting Methodology, including intelligent monitoring device,
Smart machine data acquisition unit, server and wired home seborrheic dermatitis care appliances, described intelligent monitoring device with 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, described wired home seborrheic dermatitis care appliances pass through communication device two and described server network communication.
Further, described wired home seborrheic dermatitis 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 is trained by neural network model and predicts a large amount of patient in hospital pathological data, finds seborrheic dermatitis disease
Reason and seborrheic dermatitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this several causes of disease it
Between logic association and variable, ultimately form to seborrheic dermatitis illness probability Accurate Prediction seborrheic dermatitis pathology nerve net
Network model, the present invention passes through to gather user's daily life data, and the periodicity of its data of active analysis, regularity are eventually through fat
Excessive property dermatitis pathology Neural Network model predictive user suffers from seborrheic dermatitis probability, reminds the user to be in the way of visual effect
When seek medical advice and prevent.
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 seborrheic dermatitis Forecasting Methodology based on increment type neural network model that the present invention provides,
Comprise the steps：
Step (1), acquisition hospital's seborrheic dermatitis etiology and pathology data source and the daily monitoring data of patient, thus set up fat
The daily data database of excessive property dermatitis；
Wherein daily monitoring data is 21 item data, and its 21 item data is the age, sex, heart rate, frequency of taking food, trunk
Skin conditions, the anaphylactogen material of environment, body weight, the erythra order of severity, sufferings degree, user's Allergic skin test is reported,
Smoking capacity (daily), limbs skin situation, under varying environment, the state of an illness detection of user, pursues an occupation, temperature, humidity, air matter
Volume index etc. 21 item data, the present invention sets up 21 dimensional vectors with 21 item data；
Step (2), the daily data database of seborrheic dermatitis set up according to step (1) are off-line manner to nerve net
Network model is trained, to obtain the seborrheic dermatitis 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 seborrheic dermatitis in the seborrheic dermatitis pathology neural network model training in input step (2) after doing normalized
Probabilistic forecasting, obtains seborrheic dermatitis probability, and server sends seborrheic dermatitis probability to wired home seborrheic dermatitis shield
Reason equipment；
After step (5), the seborrheic dermatitis probability of wired home seborrheic dermatitis care appliances the reception server transmission, sentence
Whether disconnected seborrheic dermatitis probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained seborrheic dermatitis, warning
Device warns to remind user, if less than 0.5, is then judged to that this user does not obtain seborrheic dermatitis；
Step (6), when user is judged to seborrheic dermatitis, user voluntarily removes examination in hospital, and by inspection result
Send back server by wired home seborrheic dermatitis care appliances, server judges whether inspection result is correct, if inspection
Come to an end fruit mistake, then explanation seborrheic dermatitis pathology Neural Network model predictive is inaccurate, if inspection result is correct, illustrates
Seborrheic dermatitis pathology Neural Network model predictive is accurate；
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 seborrheic dermatitis
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 seborrheic dermatitis), extracts 400000 records from seborrheic dermatitis daily data database table and is trained,
Every record is 21 dimensional vectors, all data before use first through normalized so as to numerical value is interval in [0,1], so
Execution following steps are trained to neural network model afterwards：
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；All input n-dimensional vectors are all input in neural network model and are trained by iteration each time, when reach regulation repeatedly
After generation number, neural network model training terminates.
Inspection result is sent back the object information of server by the wired home seborrheic dermatitis care appliances of the present invention
Form is：{ 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 seborrheic dermatitis 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 seborrheic dermatitis, and
The attention device of wired home seborrheic dermatitis care appliances does not warn then it represents that wired home seborrheic dermatitis care appliances are sentenced
Disconnected inaccurate, now execution step (6)～(7), wired home seborrheic dermatitis care appliances are sent to service object information
On device.
Present invention also offers a kind of prognoses system of described seborrheic dermatitis Forecasting Methodology, including intelligent monitoring device,
Smart machine data acquisition unit, server and wired home seborrheic dermatitis care appliances, described intelligent monitoring device with 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, described wired home seborrheic dermatitis care appliances pass through communication device two and described server network communication.
It is provided with attention device on the described wired home seborrheic dermatitis 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 seborrheic dermatitis disease
Reason and seborrheic dermatitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this several causes of disease it
Between logic association and variable, ultimately form to seborrheic dermatitis illness probability Accurate Prediction seborrheic dermatitis pathology nerve net
Network model, the present invention passes through to gather user's daily life data, and the periodicity of its data of active analysis, regularity are eventually through fat
Excessive property dermatitis pathology Neural Network model predictive user suffers from seborrheic dermatitis probability, reminds the user to be in the way of visual effect
When seek medical advice and prevent.
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 seborrheic dermatitis morbidity can not yet
With.Therefore conventional is not high by the method accuracy rate of neural network prediction seborrheic dermatitis.The present invention is directed to each equipment and uses
The neural network model training for this user is set up at family, is running after a period of time, fixed to this customer volume body by producing
Do neural network prediction model, accuracy rate is greatly improved.
When neural network model is judged by accident, error message be will be feedbacked to service by wired home seborrheic dermatitis care appliances
Device, for this user's dynamic corrections neural network model, when similar characteristics data in this user next, will not miss again
Sentence.Therefore, with the increase of use time, the judgement of the wired home seborrheic dermatitis care appliances of the present invention will be increasingly
Accurately.
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 seborrheic dermatitis Forecasting Methodology based on increment type neural network model is it is characterised in that comprise the steps：
Step (1), obtain hospital seborrheic dermatitis and cure the disease etiology and pathology data source and the daily monitoring data of patient, thus setting up fat
The daily data database of excessive property dermatitis；
Step (2), the daily data database of seborrheic dermatitis set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the seborrheic dermatitis 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 seborrheic dermatitis probability pre- in the seborrheic dermatitis pathology neural network model training in input step (2) after change process
Survey, obtain seborrheic dermatitis probability, server sends seborrheic dermatitis probability to wired home seborrheic dermatitis care appliances；
After step (5), the seborrheic dermatitis probability of wired home seborrheic dermatitis care appliances the reception server transmission, judge fat
Whether excessive property dermatitis probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained seborrheic dermatitis, attention device warns
To remind user, if less than 0.5, then it is judged to that this user does not obtain seborrheic dermatitis；
Step (6), when user is judged to seborrheic dermatitis, user voluntarily removes examination in hospital, and inspection result is passed through
Wired home seborrheic dermatitis care appliances send back server, and server judges whether inspection result is correct, if checking knot
Fruit mistake, then explanation seborrheic dermatitis pathology Neural Network model predictive is inaccurate, if inspection result is correct, seborrhea is described
Property dermatitis pathology Neural Network model predictive 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 seborrheic dermatitis pathology god
Carry out dynamic corrections through network model；
Step (8), repeat step (3)～(7).
2. a kind of seborrheic dermatitis Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, 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 seborrhea
Property dermatitis daily data database table in extract k bar record be trained, every record is a n-dimensional vector, and all data exist
Using front elder generation through normalized so as to numerical value is interval in [0,1], then execution following steps are carried out to neural network model
Training：
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 seborrheic dermatitis Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, the form that inspection result is sent back the object information of server by wired home seborrheic dermatitis care appliances is：{ inspection
Look into whether correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
4. a kind of seborrheic dermatitis Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, the increasable algorithm carrying out dynamic corrections to seborrheic dermatitis 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 newly long
The subnet going out 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 seborrheic dermatitis Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from seborrheic dermatitis, and wired home fat
The attention device of excessive property dermatitis care appliances do not warn then it represents that wired home seborrheic dermatitis care appliances judge inaccurate,
Now execution step (6)～(7), wired home seborrheic dermatitis care appliances are sent to object information on server.
6. a kind of prognoses system of seborrheic dermatitis Forecasting Methodology described in employing claim 1～6 is it is characterised in that include intelligence
Energy monitoring device, smart machine data acquisition unit, server and wired home seborrheic dermatitis care appliances, described intelligent monitoring
Equipment is connected with described smart machine data acquisition unit, described smart machine data acquisition unit pass through communication device one with described
Server network communicates, and described wired home seborrheic dermatitis care appliances are passed through communication device two and led to described server network
News.
7. according to claim 7 the prognoses system of seborrheic dermatitis Forecasting Methodology it is characterised in that described wired home fat
It is provided with attention device on excessive property dermatitis care appliances.
8. according to claim 7 the prognoses system of seborrheic dermatitis Forecasting Methodology it is characterised in that described intelligent monitoring sets
Standby include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool and Intelligent light sensing equipment.
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