The research into the impersonality and information of the Traditional Chinese-medicine Diagnosis (TCMD) is a key work to realize the Traditional Chinese-medicine (TCM) medicine examination system. So the paper studies the application of Artificial Neural Networks (NN) to TCM and uses the Genetic Algorithm (GA) to optimize the ANN, which is used to the diagnosis of TCM. The initial structure of the ANN is arbitrary and the GA is able to find a near optimal connection weight and structure of the ANN for the TCMD. In this paper, we emphatically describe t the GA to optimal the connection weight of the NN - Genetic NN, which has a relatively fixed three-layer and was used to the TCM eight principal syndromes by the tongue feature

Shi, ZZ

Wu, Y

Zhang, ZF

Zhao, MS

Zhou, CL

周昌乐

Xiamen University Institutional Repository

Genetic Neural Network for Traditional ChineseMedicineYun WuThe Graduate School ofArtificial Intelligence,XiaMen UniversityFuJian XiaMen361005, ChinaE-mail: yininwu@l26.comChangle ZhouThe Graduate School ofArtificial Intelligence,XiaMen UniversityFuJian XiaMen361005, ChinaE-mail: dozero@xmu.edu.cnZhifeng ZhangShanghai University ofTraditional Chinese MedicineShangHai201203, ChinaE-mail: rchbt@l63.comAbstract-The research into the impersonality andinformation of the Traditional Chinese-medicine Diagnosis(TCMD) is a key work to realize the TraditionalChinese-medicine (TCM) medicine examination system. Sothe paper studies the application of Artificial NeuralNetworks (NN) to TCM and uses the Genetic Algorithm (GA)to optimize the ANN, which is used to the diagnosis of TCM.The initial structure of the ANN is arbitrary and the GA isable to fmd a near optimal connection weight and structure ofthe ANN for the TCMD. In this paper, we emphaticallydescribe t the GA to optimal the connection weight of the NN- Genetic NN, which has a relatively fixed three-layer andwas used to the TCM eight principal syndromes by the tonguefeature.Keywords TCM (Traditional Chinese-medical);tongue feature; TCM eight principal syndromes;NN(Neural network), Genetic Algorithm.I LNTRODUCTIONTongue diagnosis is a very important content in theSizhen of TCM -Inspection, and is a characteristicdiagnostic method. The clinic study has proved, during thedeveloping of the diseases, the change of the tongue israpid and brilliant. It is like a mirror of the bowels, and thepathological changes of the bowels can reflect from thechange of the tongue features. So it becomes the importantevidence for the diagnosis. But because of the lack of theimpersonality and information of the TCMD, it becomesvery difficult on the road of the TCM modernization, andall the time, it is a blankness in the field of the medicineexamination. But comparing others diagnostic methods, ithas got some research result on the impersonality andinformation of the tongue diagnosis, which provide theimpersonal information criterion for the research anddevelopment of the TCM diagnostic methods based on thetongue features and promote the course of the TCM in theworld. It also can develop a new field for the TCMmedicine examination system.The TCM medical examination systems integrate thesemain technologies, such as logic, NN, GA, and others softcomputing. Its main theory is based on these: First thecharacteristics of the tongue feature, and get the logicalrule. Second construct the NN for every the TCM diagnosisrule. Because the common ANN has very weak autoadaptability, which can not satisfy the variety of the TCMclinic, So At last use the improved GA to optimize theNN(namely GNN ), and form the TCMD NN database.The TCMD NN database is information source of the TCMmedical examination system and the kernel technology.But the improved GA is the key technology to form theTCMD NN database.The GA the paper put forward that used to optimal theTCMD ANN is an improved traditional GA. At first, toachieve a good offspring, know data are divided into twosets namely training set and validation set, both of whichare used to compute the fitness of the NN. Then computethe fitness of each individual, namely one NN, andaccording to the fitness values, rank the NN from the bestto the worst, and memorize the best one and the worst one.According the rank of the NN, put the former n individualdirectly copied to next generation, the remaining offspringscome from the crossover operation. Void the localexcellent result via a crossover of the best individual andthe worst individual. In the mutation, we use the PSO[4],namely use the fitness value of the best individual and theworst individual via the mutation function to operate themutation, which is in favor of the convergent. And in orderto research the best solution of the whole solution space,the multi-GA is considered, namely get the best solutionvia select the best fitness from several GA computing.This paper is organized as follows. In section II, wepresent three-layer NN, which base on the characteristic oftongue diagnosis and the data format of TCM eightprincipal syndromes, and introduce the improved thegeneral sketch our GA design, which combine the thoughtof real coding, arithmetic recombination, PSO, etc. Insection II, we test the improved GA on tongue figure-basedTCM eight principal syndromes data, which form ispresented in section II, and describe the simulation results.Conclusions and future work has been disused in sectionIII.0-7803-9422-4/05/$20.00 C2005 IEEE3391) The ANN based on the TCM tongue features(1) The structure of the tongue figure-based TCM eightprincipal syndromes ANNFor using the improved GA to optimize the value and thestructure of the tongue figure-based eight principalsyndromes NN, we initialize an arbitrary ANN, which hasthree layers. The three layers are the input layer, the hiddenlayer and the output layer. The input layer has the valuecontact with the hidden layer, and the hidden layer has thecontact with the output layer. The construct shows infigurel.vabeIrplutqI Hiddenlayer OutpatlayerFigure 1. the three-layer feed-backANN's structureThe number of the input of the training set confirms thenumber of the input cell. The number of the hidden nervecell is confirmed by our experience. The number of theresult confirms the number of the output cell.(2) The data structure of the tongue feature-based TCMeight principal syndromeswe use the tongue figure(or tongue symptoms) to be theexample to diagnosis the "Eight principal syndromes",which include eight syndrome, such as "Exterior, Interior,Cold, Hot, Deficiency, Excess". The detail context canbeen seen this table.Tongue figuresyndrome value Bodycolor shape stateexterior 1 Light constancxtenor red constancyinterior 2 inconstancy inconstacold 3 whitehot 4 crimsonlightdeficiency 5 white, tendercrimsonblueness oldexcess 6 purple old__default 7Table 1 (a)Tongue figuresyndrome value Coatingcolor properexterior 1 white, yellow thininterior 2 thickcold 3 white moisthot 4 yellow drydeficiency 5 X exfoliativeexcess 6 thick, greasydefault 7Table 1 (b)Explain to Table 1 1. the blank in the table means thesyndrome has not distinct symptoms or the symptoms cannot help much to confirm the syndrome. 2. the defaultmeans the symptoms is not enough, so the syndrome cannot be ensured. 3. this table is made based on thereference book [1] in the catalog, in order to confirm that thetechnology of the ENN is valid.(3) Date the symptoms, such asTongue Color: light redt }; light white{2}; crimsont3};blueness purple{4}Tongue Shape: constancy{ 1), inconstancy{2}, tender{3),old{4}Tongue State: constancy{ 1 }, inconstancy{2}Coating Color: whitet 11, yellow{2}Coating Proper: thin{l}, thick{2}, moist{3}, dry{4}lgreasy{5}, exfoliative{6}If there are not symptoms to the syndromes, the valuethat the symptom is corresponding in the table is zero.(4) Construct data set of the input stylebook and outputstylebook.[Tongue color, Tongue shape, Tongue state, Coatingcolor, Coating Proper] /[Eight principal syndromes],according tie table 1, the stylebook data like this:Exterior: [1, 1, 1, 1, 1]/[1][1, 1, 1, 2, 1]/[1]Interior: [0, 2, 2, 0, 2]/[2]Cold: [2, 0, 0, 1, 3]/[3]Hot: [3, 0, 0, 2, 4]/[4]Deficiency: [2, 3, 0, 0, 6]/[5]; [3, 3, 0, 0, 6]/[5]Excess: [4, 4, 0, 0, 2]/[6]; [4,4, 0, 0, 5]/[6]default: [0, 0, 0, 0, 0]/[7]In order to increase the quantity of the input training set,we pick up some data form the inference book[l] and theclinic data of the author[3], which has six kinds of TCMeight principal syndromes' data.2) The GA for optimizing the TCMD ANNThe main steps of the GA for optimizing the TCMDANN are:(1) Real-valued two-matrix encoding. A network isrepresented as a real-valued matrix, in which cij is zero or isa very small number if there no link from the input neuron ito the hidden neuron j, which can encode both the structure340the weights.(2) Initialization. Using the conventional method,randomly generate an initial population of networks withnumber of input neurons and the hidden neurons, theconnection weights assigned at random. Before into nextstep, each network is partially trained, this can avoidoverstraining.(3) Fitness function. Known stylebook set is divided intotwo sets namely training set (Ts) and validation set (Vs).After partially trained, compute the fitness of each NN, andrank them from the best to the worst memorized the bestNN of this generation and modify the NN of the allgenerations. The fitness function is:f(x) =ax E(Ts)+ (1-a)xE(Vs)E(T)= MSE (x)(4) Genetic operation. Directly copy K individuals (Kusually is twenty percent of the population of the NN) havebest fitness to next generation, for keeping the best gene.The remaining offspring comes from the crossoveroperation. For escaping the local excellent result, bringingone offspring by crossover between the best individual andthe worst individual.(D Selection. According the sorted individuals number,the ith individual is selected with a probability ofi=1thexis a random number, and if thea<pi, the individual isselected.(9) Crossover. Select two individual, and crossover thetwo individual via the mathematic recombination.Assuming thatX ={x, x2,..., x,} and Y={yI,y2 ... yjaretwo individual for crossover, and the xi, yi are thegenes in the two individual. The crossover is implementedas follows: zi = h Xx +(1- h,)xythe hi E (0,1) is a random number. Subsequently, eachNN is partially trained.(© Mutation. Select individuals and conduct mutationsaccording to the current mutation rate Pm. Considering themethod of the TCM case learning which are always appliedin the TCM expert systems, so the mutation uses thethought of PSO, namely according the fitness of the bestindividual and the worst individual, randomly select twoindividuals, such as i and j, then mutation is implementedas follows:Xij =Xij +0 x (f(pg(t))- f (Xij))+ 02 X (f (pd (t))-f (x,j ))the pg (t) is the best evolutionary NN of the allgeneration, Pd (t) is the best evolutionary NN of thisgeneration, /1,0i2 E [-1,11 are random number. Thismethod simulates the thought of TCM case learning, and ispropitious to the convergence of the GA.(5) Ending condition. In the GA computing, if neither themaximal evolutionary epoch is reached nor the unchangedtimes of the fitness of the best NN is reached, go to (2). Ifneither the performance of the best NN meets therequirement or the unchanged times of the fitness of thebest NN, which get from one GA computing is reached, goto(1).II. MAIN RESULTSAccording to the data form of the tongue figure-basedeight principal syndromes, we initial a three-layerfeed-back NN, the output layer has five input cells, thehidden layer has ten nerve cells at initially, and the outputlayer has seven output cells.When the GA is computing, the partial training graphs ofthe TCM eight principal syndromes NN is different, suchas:3413 ) the anaphase of the GA:The partial computing result of the optimized the tonguefigure-based TCM eight principal syndromes NN isfollowing:1 ) the partial computing result of training set:Inputdata Com-i'ting ExactInput da result result. [1,1,1,1,1] 1 1[0,2,2,0,2] 2 2[2,0,0,1,3] 3 3[3,0,0,2,4] 4 4[3,3,0,0,6] 5 5[4,4,0,0,2] 6 6[0,0,0,0,0] 7 7[0,2,2,0,0] 2 2[4,0,0,0,2] 6 6[0,0,0,0,6] 5 5Table 2.2) the partial computing result of valuable set:Input data Computing ExactInpu ~~result result[3,0,0,0,4] 4 4[4,4,1,1,5] 6 6[3,1,1,2,4] 4 4[2,1,1,1,3] 5 3[1,0,0,0,0] 6 7[3,0,0,2,0] 6 4[4,4,1,0,0] 6 6[0,3,0,0,6] 5 5[0,1,0,1,1] 3 1[1,2,2,0,0] 2 2Table 3.excellent and more stable than the common ANN that isused to TCMD.m. CONCLUSIONSThis paper discusses the GA optimize the ANN, which isused to TCMD TCM eight principal syndromes basedon the tongue feature. Using the arithmetic recombinationand the theory of the PSO to help the GA escape from poorlocal excellent value and improve the convergence. At thesame time, in order to improve the computing correctnessof the validation set, we make the computing value of thevalidation set act as one of factors, which are used tocompute the fitness. After the simulating, the Genetic NNto TCMD has better performance than initial NN, speciallyin the adaptability and the stabilization. If the Genetic NNhas automatic structure based on the stylebook data, it canperform better, which is our next work.REFERENCES[1] Wenfeng Zhu, Traditional Chinese Medicine DiagnosticsMBeijing: Chinese Medicaments Press, 2004[2] Nenggan Zhang, TCM-SIRD:An Intergrated Aided System forTraditional Chinese Medicine Sizheng[J] , 2004 IEEEInternational Conference on System, Man and Cybernetics, 2004,(3864-3868)[3] Xuezhong Zhou, TCMMDB:A Distributed MultiDatabase QuerySystem and It's Key Technique Implement, 2004IEEE(1095-1098)[4] Chia-Feng Juang, On The Hybrid of Genetic Algorithm and ParticleSwarm Optimization For Evolving Recurrent Neural Network,2004, IEEE (2285-2288)[5] Z.-H Tan, Hybrid evolutionary approach for designing neuralnetworks for classification, ELECTRONICS LETTERS 22nd July2004 Vol. 40 No. 15[6] Chonghong Dong, Matlab, Neural Networksand Application[M]Beijing: National Defence Industry Press, 2005After optimized by the GA, the exact rate of the TCMeight principal syndromes NN is 100% to the training set,the exact rate to the valuable set is 60%, which is more342

Genetic neural network for Traditional Chinese medicine

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Genetic neural network for Traditional Chinese medicine

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