CN106250714A - A kind of renal calculus Forecasting Methodology based on increment type neural network model and prognoses system - Google Patents
A kind of renal calculus Forecasting Methodology based on increment type neural network model and prognoses system Download PDFInfo
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Abstract
The invention discloses a kind of renal calculus Forecasting Methodology based on increment type neural network model, comprise the steps: to set up the daily data database of renal calculus；Neural network model is trained；Gather daily life data to send to server, preserve to the daily data logger of user；The daily data logger of user extracts data on the same day, forms n-dimensional vector, input in renal calculus pathology neural network model after doing normalized and carry out renal calculus probabilistic forecasting；Wired home renal calculus care appliances judges that whether renal calculus probit is more than 0.5；User is judged to renal calculus, and user removes examination in hospital voluntarily, and by wired home renal calculus care appliances, inspection result is sent back server, and server judges to check that result is the most correct；Perform increasable algorithm when checking result mistake, neural network model is carried out dynamic corrections.The present invention predicts accurately, makes neural network model to measure for each user.
Description
Technical field
The invention belongs to field of medical technology, particularly relate to a kind of renal calculus based on increment type neural network model pre-
Survey method and prognoses system.
Background technology
Present Domestic is each health management system arranged is respectively provided with renal calculus prediction and evaluation, and its prediction mode used is data
Join.Its principle is that by system matches fixed data, then personal lifestyle data entry system is shown ill probability.But due to people
Body and the complexity of disease, unpredictability, in the form of expression of bio signal and information, Changing Pattern (Self-variation with
Change after medical intervention) on, it being detected and signal representation, the data of acquisition and the analysis of information, decision-making etc. are all in many ways
All there is extremely complex non-linear relationship in face.So using traditional Data Matching can only be data examination blindly, it is impossible to
Judging the logic association between data and data and variable, the codomain deviation obtained is big, causes the specificity ten of system prediction
Point poor, domestic health management system arranged effectively individual renal calculus cannot be carried out Accurate Prediction so current.
Major part is all to use BP neural network model to renal calculus prediction before this, but when new detection data generation
Time, it is necessary to again training neural network model, operation efficiency is extremely low.And after system user scale increases, server
Will be unable to complete in time training mission.
Summary of the invention
The purpose of the present invention is that and overcomes the deficiencies in the prior art, it is provided that a kind of based on increment type neural network model
Renal calculus Forecasting Methodology and prognoses system, the present invention by neural network model training predict a large amount of patient in hospital pathology numbers
According to, find renal calculus pathology and renal calculus earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this
Logic association between several the causes of disease and variable, the renal calculus pathology ultimately forming probability Accurate Prediction ill to renal calculus is neural
Network model, the present invention by gathering user's daily life data, the periodicity of its data of active analysis, regular eventually through
Renal calculus pathology Neural Network model predictive user suffers from renal calculus probability, reminds user instant just in the way of visual effect
Doctor, constantly revises neural network model, to set for each when Neural Network model predictive is inaccurate by increasable algorithm
Standby user is set up and is trained the neural network model for this user, along with the increase of the time of use, to set up this customer volume
The neural network model that body is customized, accuracy rate is greatly improved.
To achieve these goals, the invention provides a kind of renal calculus prediction side based on increment type neural network model
Method, comprises the steps:
Step (1), acquisition hospital renal calculus etiology and pathology data source and patient's daily monitoring data, thus set up renal calculus
Daily data database；
Step (2), the daily data database of renal calculus set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the renal calculus pathology neural network model trained；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life that will gather
Live data sends to server, and the daily life data of user are preserved to the daily data logger of user by server；
Step (4), from the daily data logger of user, extract data on the same day, form n-dimensional vector, and n-dimensional vector is done
The renal calculus pathology neural network model trained in input step (2) after normalized carries out renal calculus probabilistic forecasting,
Obtaining renal calculus probability, renal calculus probability is sent to wired home renal calculus care appliances by server；
After step (5), wired home renal calculus care appliances receive the renal calculus probability that server transmits, it is judged that renal calculus
Whether probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained renal calculus, and attention device warns to remind user,
If less than 0.5, then it is judged to that this user does not obtain renal calculus；
Step (6), when user is judged to renal calculus, user removes examination in hospital voluntarily, and inspection result is passed through
Wired home renal calculus care appliances sends back server, and server judges to check that result is the most correct, if checking that result is wrong
By mistake, then explanation renal calculus pathology Neural Network model predictive is inaccurate, if checking that result is correct, then and explanation renal calculus pathology god
Through network model's prediction accurately；
Step (7), when checking result mistake, extract from the daily data logger of user the record in m days preserve to
In incremental data table, when the record quantity in incremental data table is more than h bar, perform increasable algorithm, to renal calculus pathology god
Dynamic corrections is carried out through network model；
Step (8), repetition 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 renal calculus daily data database table and is trained, and every record is a n-dimensional vector, all
Data are before use first through normalized so that it is numerical value is interval in [0,1], then perform following steps to neural network model
It is trained:
1) one n-dimensional vector of input is to neural network model, calculates all of weight vector in neural network model defeated to this
Entering the distance of n-dimensional vector, closest neuron is triumph neuron, and its computing formula is as follows:
Wherein: WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) adjusting the weight vector of neuron in 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
Territory；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 repeatedly
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt it 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 all are input in neural network model be trained by iteration each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
Further, wired home renal calculus care appliances will check that result sends back the form of the object information of server
For: { checking whether correct, blood glucose value }, server is after receiving object information, it is judged that check that result is the most correct.
Further, the increasable algorithm that renal calculus pathology neural network model carries out dynamic corrections is:
Vectorial for every in incremental data table V{V1,V2,…,Vn, it is sent in neural network model learning function enter
Row study, learning procedure is as follows:
1) first each to output layer weight vector is composed little random number and does normalized, then utilizes input mode vector V
Meansigma methods Avg (V), be initialized as in neural network model the 0th layer the weights of unique neuron, and be set to nerve of winning
Unit, calculates its quantization error QE；
2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the power of these 4 neurons is initialized
Value；The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, 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) from VX, select a vectorial VXiDo following judgement:
If VXiFor the data of not tape label, then calculating the Euclidean distance of it and 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 neuron work that value is maximum
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ that wins
{VXi, calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. the most predetermined frequency of training, then
Go to step 4)；
6) calculate adjusted after this neural network model in quantization error QE of each neuroni, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting 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 kind set on neuron；
Then judge:
If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, turn step
Rapid 4)；
If Ei> 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 grown increases in the subnet queue of Layer+1 layer；
If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；
7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding out, iteration operating procedure 3)～5) to it again
Being 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 and oneself have suffered from renal calculus, and intelligent
The attention device of family's renal calculus care appliances does not warn, then it represents that wired home renal calculus care appliances judges inaccurate, this
Shi Zhihang step (6)～(7), wired home renal calculus care appliances is sent to object information on server.
Present invention also offers the prognoses system of a kind of described renal calculus Forecasting Methodology, including intelligent monitoring device, intelligence
Device data acquisition device, server and wired home renal calculus care appliances, described intelligent monitoring device and described smart machine
Data acquisition unit is connected, and described smart machine data acquisition unit is by communication device one and described server network communication, institute
State wired home renal calculus care appliances by communication device two and described server network communication.
Further, described wired home renal calculus care appliances is provided with attention device.
Further, described intelligent monitoring device include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligence horse
Bucket and Intelligent light sensing equipment.
Beneficial effects of the present invention:
1, the present invention by neural network model training predict a large amount of patient in hospital pathological data, find renal calculus pathology with
Renal calculus earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic between these several the causes of disease
Association and variable, ultimately form the renal calculus pathology neural network model of probability Accurate Prediction ill to renal calculus, and the present invention is led to
Crossing collection user's daily life data, the periodicity of its data of active analysis, regularity are eventually through renal calculus pathology nerve net
Network model prediction user suffers from renal calculus probability, reminds user's instant hospitalizing and prevention in the way of visual effect.
2, constantly revise neural network model when Neural Network model predictive is inaccurate by increasable algorithm, with for
Each equipment user sets up and trains the neural network model for this user, along with the increase of the time of use, to set up this
The neural network model that user makes to measure, accuracy rate is greatly improved.
Accompanying drawing explanation
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
In having technology to describe, the required accompanying drawing used is briefly described, it should be apparent that, the accompanying drawing in describing below is only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to
Other accompanying drawing is obtained according to these accompanying drawings.
Fig. 1 is the flow chart of the embodiment of the present invention.
Detailed description of the invention
Below in conjunction with the accompanying drawings invention is further illustrated, but be not limited to the scope of the present invention.
Embodiment
As it is shown in figure 1, a kind of based on increment type neural network model the renal calculus Forecasting Methodology that the present invention provides, including
Following steps:
Step (1), acquisition hospital renal calculus etiology and pathology data source and patient's daily monitoring data, thus set up renal calculus
Daily data database；
The most daily monitoring data are 12 item data, and its 12 item data is body temperature, heart beating, heart rate, body fat, the amount of drinking water and frequency
Rate, total urination time, urine color, body weight, the length of one's sleep and quality, every day 12 item data such as travel distance, the present invention is with 12
Data set up 12 dimensional vectors；
Step (2), the daily data database of renal calculus set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the renal calculus pathology neural network model trained；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life that will gather
Live data sends to server, and the daily life data of user are preserved to the daily data logger of user by server；
Step (4), from the daily data logger of user, extract data on the same day, form 12 dimensional vectors, and to 12 dimensional vectors
The renal calculus pathology neural network model trained in input step (2) after doing normalized carries out renal calculus probability pre-
Surveying, obtain renal calculus probability, renal calculus probability is sent to wired home renal calculus care appliances by server；
After step (5), wired home renal calculus care appliances receive the renal calculus probability that server transmits, it is judged that renal calculus
Whether probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained renal calculus, and attention device warns to remind user,
If less than 0.5, then it is judged to that this user does not obtain renal calculus；
Step (6), when user is judged to renal calculus, user removes examination in hospital voluntarily, and inspection result is passed through
Wired home renal calculus care appliances sends back server, and server judges to check that result is the most correct, if checking that result is wrong
By mistake, then explanation renal calculus pathology Neural Network model predictive is inaccurate, if checking that result is correct, then and explanation renal calculus pathology god
Through network model's prediction accurately；
Step (7), when checking result mistake, extract from the daily data logger of user the record in 7 days preserve to
In incremental data table, when the record quantity in incremental data table is more than 100, perform increasable algorithm, to renal calculus pathology
Neural network model carries out dynamic corrections；
Step (8), repetition step (3)～(7).
The input layer of the neural network model of the present invention is 12 nodes, and hidden layer number is 25, and output layer is 1 node
(i.e. the probability of renal calculus), extracts 400000 records from renal calculus daily data database table and is trained, every record
Being 12 dimensional vectors, all data are before use first through normalized so that it is numerical value is interval in [0,1], then performs such as
Neural network model is trained by lower step:
1) one 12 dimensional vector of input are to neural network model, calculate all of weight vector in neural network model defeated to this
Entering the distance of 12 dimensional vectors, closest neuron is triumph neuron, and its computing formula is as follows:
Wherein: WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) adjusting the weight vector of neuron in 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
Territory；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 repeatedly
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt it 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 all are input in neural network model be trained by iteration each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
The wired home renal calculus care appliances of the present invention will check that result sends back the form of the object information of server
For: { checking whether correct, blood glucose value }, server is after receiving object information, it is judged that check that result is the most correct.
The increasable algorithm that renal calculus pathology neural network model carries out dynamic corrections 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 enter
Row study, learning procedure is as follows:
1) first each to output layer weight vector is composed little random number and does normalized, then utilizes input mode vector V
Meansigma methods Avg (V), be initialized as in neural network model the 0th layer the weights of unique neuron, and be set to nerve of winning
Unit, calculates its quantization error QE；
2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the power of these 4 neurons is initialized
Value；The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, 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) from VX, select a vectorial VXiDo following judgement:
If VXiFor the data of not tape label, then calculating the Euclidean distance of it and 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 neuron work that value is maximum
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ that wins
{VXi, calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. the most predetermined frequency of training, then
Go to step 4)；
6) calculate adjusted after this neural network model in quantization error QE of each neuroni, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting 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 kind set on neuron；
Then judge:
If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, turn step
Rapid 4)；
If Ei> 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 grown increases in the subnet queue of Layer+1 layer；
If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；
7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding out, iteration operating procedure 3)～5) to it again
Being 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 and oneself have suffered from renal calculus, and intelligent
The attention device of family's renal calculus care appliances does not warn, then it represents that wired home renal calculus care appliances judges inaccurate, this
Shi Zhihang step (6)～(7), wired home renal calculus care appliances is sent to object information on server.
Present invention also offers the prognoses system of a kind of described renal calculus Forecasting Methodology, including intelligent monitoring device, intelligence
Device data acquisition device, server and wired home renal calculus care appliances, described intelligent monitoring device and described smart machine
Data acquisition unit is connected, and described smart machine data acquisition unit is by communication device one and described server network communication, institute
State wired home renal calculus care appliances by communication device two and described server network communication.
It is provided with attention device on the described wired home renal calculus care appliances of the present invention.
The described intelligent monitoring device of the present invention include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool
With Intelligent light sensing equipment etc..
The present invention predicts a large amount of patient in hospital pathological data by neural network model training, finds renal calculus pathology and kidney
Calculus earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic between these several the causes of disease is closed
Connection and variable, ultimately form the renal calculus pathology neural network model of probability Accurate Prediction ill to renal calculus, and the present invention passes through
Gathering user's daily life data, the periodicity of its data of active analysis, regularity are eventually through renal calculus pathology neutral net
Model prediction user suffers from renal calculus 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, and hardware configuration is low, thus sells
Valency is the lowest.
The present invention carries communication device one and communication device two, by wifi from being dynamically connected the Internet, and can protect for a long time
Hold online.Various intelligent monitoring devices can easily access present device by the mode such as network or bluetooth, sets in acquisition
The daily life data of the monitoring of intelligent monitoring device, the data that therefore present device obtains can be automatically uploaded after standby mandate
Be real-time, accurately, polynary.
Owing to everyone physical trait is different, the data characteristics shown during renal calculus morbidity also can be different.Cause
This is conventional the highest by the method accuracy rate of neural network prediction renal calculus.The present invention is directed to each equipment user and set up training
Go out the neural network model for this user, running after a period of time, this user is being made generation to measure neutral net
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 renal calculus care appliances,
For this user's dynamic corrections neural network model, when next time, similar characteristics data occurred in this user, will not judge by accident again.Cause
This, along with the increase of the time of use, the judgement of the wired home renal calculus care appliances of the present invention will be more and more accurate.
The ultimate principle of the present invention, principal character and advantages of the present invention have more than been shown and described.The technology of the industry
Personnel, it should be appreciated that the present invention is not restricted to the described embodiments, simply illustrating this described in above-described embodiment and description
The principle of invention, the present invention also has various changes and modifications without departing from the spirit and scope of the present invention, and these become
Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and
Equivalent defines.
Claims (8)
1. a renal calculus Forecasting Methodology based on increment type neural network model, it is characterised in that comprise the steps:
Step (1), obtain hospital renal calculus and cure the disease etiology and pathology data source and patient's daily monitoring data, thus set up renal calculus
Daily data database；
Neural network model is entered by step (2), the daily data database of renal calculus set up according to step (1) off-line manner
Row training, to obtain the renal calculus pathology neural network model trained；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life number that will gather
According to sending to server, the daily life data of user are preserved to the daily data logger of user by server；
Step (4), from the daily data logger of user, extract data on the same day, form n-dimensional vector, and n-dimensional vector is done normalizing
The renal calculus pathology neural network model trained in input step (2) after change process carries out renal calculus probabilistic forecasting, obtains
Renal calculus probability, renal calculus probability is sent to wired home renal calculus care appliances by server；
After step (5), wired home renal calculus care appliances receive the renal calculus probability that server transmits, it is judged that renal calculus probability
Whether value is more than 0.5, if greater than 0.5, is then judged to that this user obtained renal calculus, and attention device warns to remind user, if
Less than 0.5, then it is judged to that this user does not obtain renal calculus；
Step (6), when user is judged to renal calculus, user removes examination in hospital voluntarily, and will check that result is by intelligence
Family's renal calculus care appliances sends back server, and server judges to check that result is the most correct, if checking result mistake, then
Illustrate that renal calculus pathology Neural Network model predictive is inaccurate, if checking that result is correct, then explanation renal calculus pathology nerve net
Network model prediction is accurate；
Step (7), when checking result mistake, the record extracted from the daily data logger of user in m days preserves to increment
In tables of data, when the record quantity in incremental data table is more than h bar, perform increasable algorithm, to renal calculus pathology nerve net
Network model carries out dynamic corrections；
Step (8), repetition step (3)～(7).
A kind of renal calculus Forecasting Methodology based on increment type neural network model the most 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 renal calculus day
Extracting k bar record in regular data database table to be trained, every record is a n-dimensional vector, and all data are first before use
Through normalized so that it is numerical value is interval in [0,1], then perform following steps and neural network model be trained:
1) one n-dimensional vector of input is 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 triumph neuron, and its computing formula is as follows:
Wherein: WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) adjusting the weight vector of neuron in 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 it is the distance of neuron j and triumph neuron；σ (t) is as the function that the time successively decreases；
All input n-dimensional vectors all are input in neural network model be trained by iteration each time, when the iteration time reaching regulation
After number, neural network model training terminates.
A kind of renal calculus Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature exists
In, the form of the object information that inspection result sends back server is by wired home renal calculus care appliances: { just check whether
Really, blood glucose value }, server is after receiving object information, it is judged that check that result is the most correct.
A kind of renal calculus Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature exists
In, the increasable algorithm that renal calculus pathology neural network model carries out dynamic corrections is:
Vectorial for every in incremental data table V{V1,V2,…,Vn, it is sent in neural network model learning function learn
Practising, learning procedure is as follows:
1) first each to output layer weight vector is composed little random number and does normalized, then utilizes the flat of input mode vector V
Average Avg (V), is initialized as in neural network model the 0th layer the weights of unique neuron, and is set to triumph neuron, meter
Calculate its quantization error QE；
2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, 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 these 4 neurons are initialized；Will
The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, 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) from VX, select a vectorial VXiDo following judgement:
If VXiFor the data of not tape label, then calculate the Euclidean distance of it and each neuron, the nerve that chosen distance is the shortest
Unit is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe neuron of value maximum is as obtaining
Victory neuron, updates this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest neuron i is as obtaining
Victory neuron；
5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ { VX that winsi,
Calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. the most predetermined frequency of training, then go to step
4)；
6) calculate adjusted after this neural network model in quantization error QE of each neuroni, neuronal messages entropy EiAnd son
The average quantization error MQE of net, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting 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 kind set on neuron；
Then judge:
If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, go to step 4)；
If Ei> 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 grown increases in the subnet queue of Layer+1 layer；
If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；
7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding 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.
A kind of renal calculus Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature exists
In, if user includes health check-up by other means and checks oneself, learn and oneself have suffered from renal calculus, and the nursing of wired home renal calculus
The attention device of equipment does not warn, then it represents that wired home renal calculus care appliances judges inaccurate, now perform step (6)～
(7), wired home renal calculus care appliances is sent to object information on server.
6. one kind uses the prognoses system of renal calculus Forecasting Methodology described in claim 1～6, it is characterised in that include intelligence prison
Control equipment, smart machine data acquisition unit, server and wired home renal calculus care appliances, described intelligent monitoring device and institute
Stating smart machine data acquisition unit to be connected, described smart machine data acquisition unit is by communication device one and described server net
Network communication, described wired home renal calculus care appliances is by communication device two and described server network communication.
The prognoses system of renal calculus Forecasting Methodology the most according to claim 7, it is characterised in that described wired home renal calculus
Attention device it is provided with on care appliances.
The prognoses system of renal calculus Forecasting Methodology the most according to claim 7, 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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