Using Immune Genetic Algorithm to Optimize BP Neural Network and Its Application Peng-fei LIU1,Qun-tai SHEN1 and Jun ZHI2,*

Transcription

1 Advances n Computer Scence Research (ACRS), volume 54 Internatonal Conference on Computer Networks and Communcaton Technology (CNCT206) Usng Immune Genetc Algorthm to Optmze BP Neural Network and Its Applcaton Peng-fe LIU,Qun-ta SHEN and Jun ZHI2,* School of Informaton Scence and Engneerng, Central South Unversty, Chna 2 Beng nsttute of remote sensng nformaton, Chna *Correspondng author Keywords: Immune genetc algorthm, Neural network, Image recognton Abstract: It proposes a target classfcaton and recognton method based on mmune genetc neural. Immune genetc algorthm s adopted to optmze the ntal weghts and thresholds of BP (Back Propagaton) network. Respectvely use tradtonal BP neural and mmune genetc neural network algorthm to tran network, untl the convergence error precson of neural network reaches a pre-set requrement. Smulaton results show that wth the same hdden layer nodes and error precson request, t proposes mmune genetc neural network algorthm for mage recognton, ts effect s better than that of tradtonal BP algorthm, mproves the convergence speed of BP neural network, and reduces the tranng tme. The recognton rate mproves to some extent. Immune Genetc Algorthm Genetc algorthm s a stochastc search algorthm, groups search and nformaton exchanges between ndvduals are acheved through crossover and mutaton operators []. It ensures the entre populaton evolutonary drecton under the "survval of the fttest" selecton mechansm. But crossover and mutaton operators are carryng out the random teratve search under certan probablty condtons. In the evoluton of each ndvdual, but also t may produce degradaton. Immunzaton s a phenomenon of organsms autonomously aganst vruses to mantan ther physcal actvty stable and equlbrum. When the mmune system produces "ntal mmune response" wth a specfc antgen, part of the antbodes become memory cells, and remember the antgen [2]. When they meet agan, t wll produce "twce mmune response and clear the antgen quckly. Usng the remembered antgen recognton functon n the genetc algorthm, t can speed up the search. Immune genetc algorthm combnes mmune algorthm wth genetc algorthm, make the obectve functon of practcal problem correspond to an antgen, problem soluton correspond to the antbody; use antgen and antbody affnty to represent the approxmaton degree of feasble soluton and optmal soluton [3]. For a partcular problem, the algorthm randomly generates a set of ntal antbody, and then calculates the ftness of each antbody, acheve the envronmental adaptaton by promoton and restran between antbodes. BP Neural Network BP neural network s also known as back propagaton neural network. It s dvded nto the nput layer, ntermedate layer (hdden layer) and output layer [4]. There s no connecton between each layer of neurons; the nput layer and output layer are Copyrght 207, the Authors. Publshed by Atlants Press. Ths s an open access artcle under the CC BY-NC lcense ( 628

2 connected wth the outsde world. The BP neural s a knd of supervsed learnng algorthm. Method of Immune Genetc Algorthm to Optmze BP Neural Network Weghts Tradtonal BP network s senstve to the ntal weghts and threshold [5]. There are defects of slow convergence speed and easy convergence to the local mnmum. Immune genetc algorthm has stronger global searchng ablty. It can converge to the global optmal soluton, and t s robust. A combnaton of both can well overcome network defcences and make BP network have a faster convergence and stronger learnng ablty. Therefore, mmune genetc algorthm s adopted to optmze the ntal weghts and thresholds of BP network. The optmzaton process of mmune genetc algorthm ncludes ndvdual codng and decodng, ftness calculaton, promoton and nhbton of antbody concentraton, genetc update of antbody populaton. The concrete method s as follows. Antbody Gene Codng and Decodng Real codng has the advantage of hgh precson and large space search. Compared wth bnary codng, coded decmal converson step s omtted n the real codng. Codng s smple and ntutve; search effcency s mproved. Therefore, weghts and thresholds between the neurons of BP network are encoded wth real number codng method. It drectly takes weghts and thresholds as the value of ndvdual genes. Assumng that neurons of nput layer, hdden layer and output layer n the BP network are respectvely n, m, l, weght between nput layer and hdden layer s w,weght between hdden layer and output layer s w,threshold of hdden layer s b, threshold of output layer s b k,where,,, k respectvely expresses the ndex number of nput layer node, hdden layer node and output layer node. So the codng method for populaton ndvduals s( w w wmn, b b bm, w wk wml, b bk bl ),each ndvdual represents a knd of ntal weghts and thresholds scheme of BP network. The ndvdual s coded accordng to the sequence of weghts and thresholds, so when decodng, we ust need to read correspondng number of genes for populaton ndvduals. For example, w reads former m ( ) genetc data, b reads m n genetc data. Desgn of Antbody Ftness Functon Ftness functon of mmune genetc algorthm s used to determne the adapton of populaton ndvduals durng the optmzaton process. Ftness functon drectly determnes the optmal soluton. Usng mmune genetc algorthm to optmze BP network, the fnal results should be combned wth the output of BP network. So frstly we consder usng error performance functon of BP network as the ftness functon of evolutonary ndvduals. Set that the number of antbody populaton s n. Each antbody corresponds to a knd of network, as a combned network structure. Use error performance functon to calculate the correspondng error of each antbody. Ftness functon F x ) s defned as the error performance functon of neural ( network, that s E x ). Namely: ( k 629

3 F ( x ). () E( x ) Where P S n n 2 E( x ) ( T Y ). (2) 2P n P s the total number of tranng samples; S s the output layer nodes of BP neural network; Y and n T are respectvely the actual output and expected output of No.n n tranng sample on the No. output node. s a constant greater than 0. Concentraton of the Antbody Make the parameter to be desgned mapped for the allele n antbody, t s expressed as G (). n antbodes consttute a non-empty mmune system set X. Set d as the dstance measurement between antbody and other antbodes n the collecton X. k k 2 d ( G ( ) G ( )). k Concentraton of antbody s nversely proportonal to concentraton A as follows. (3) d, set antbody A. d (4) The larger A s, the more smlar antbodes there are n the collecton X, otherwse A s smaller. Promoton and Inhbton of the Antbody Introduce the concentraton factor to adust the probablty of antbody concentraton. Suppress the antbodes wth hgh concentraton and low affnty; promote the antbodes wth low concentraton and hgh affnty. Probablty selecton formula based on the antbody concentraton s as follows. P( ) ( A ) F( ). (5) Where, s an adustable parameter between 0 and. In the collecton X, the larger the antbody concentraton s, the lower the affnty s, and the smaller the selecton probablty s. Otherwse the larger the selecton probablty s. Genetc Update of Antbody Populaton Crossover. Includng cross way and cross probablty. In each generaton of the populaton, the hgher the crossover probablty s, the faster the new structure ntroduces. Carry on the crossover to the ndvduals G and G wth probablty P c, we can get new ndvduals G ' and G '. Drectly copy the ndvduals of no crossover operaton. 2Mutaton operaton. When the ftness change s too small, t needs to ncrease the mutaton probablty, and ncrease the dversty of samples, so that the populaton can 630

4 ump out of local mnmum. When evoluton meets the requrements, reman the mutaton probablty. Mutaton probablty adaptvely changes accordng to the ftness varaton and evoluton generatons. The selecton of mutaton pont s determned by populaton dversty measure. Mutate the ndvdual G and generate G wth probablty P m. Dversty measures of populaton and mutaton probablty are defned as follows. n n n n m( ) (max al, ( al ) mn al, ( al ) ) n. (6) m () p () l m (). (7) Where, m () s the dversty measure of No.. l s the total number of genes. Termnaton Condton Judgment Output the optmzaton results. Here the termnaton condton s that the error between the algorthm calculaton result and the actual result s less than a certan fxed value. ' sample Smulaton Analyss Col-20 (Columba obect mage lbrary) mage database contans 20 obects. Frstly fx the camera, and then put the obect on a rotatng platform. Take the pctures at ntervals of 5, and get obect mages of 72 dfferent perspectves. Each obect only lsts three mages of dfferent vew angle after translaton, rotaton and scalng. Each obect represents a class, the sample s dvded nto 3 classes, and each class has 72 mages. Use 36 mages of them to tran the neural network, the rest 36 mages test the traned neural network. Separately use 36 sample mages of each type to test the mmune genetc neural and the tradtonal BP neural n order to dentfy the recognton effect of the network as shown n Table. Table : Recognton rate comparson of mmune genetc neural and the tradtonal BP neural tranng sample / total samples correct dentfcaton / total recognton Recognton rate (%) tradtonal BP neural mmune genetc neural tradtonal BP neural mmune genetc neural Ob 36/72 32/36 34/ Ob2 36/72 3/36 33/ Ob3 36/72 30/36 32/ It can be seen: t proposes mmune genetc neural for mage recognton, ts effect s better than that of tradtonal BP algorthm, mproves the convergence speed of BP neural network, and reduces the tranng tme. On the recognton rate, compared wth the tradtonal BP algorthm, mmune genetc neural mproves to some extent. But when the obect scales down to /4 63

5 or more, the recognton rate decreases, the man reason s that the target n the rotaton, scalng dstorton. Reference: [] W. Huang and L. Jao. Artfcal Immune Kernel Clusterng Network for Unsupervsed Image Segmentaton. Progress n Natural Scence, [2] A.J. Graaff and A. P. Engelbrecht. Usng Sequental Devaton to Dynamcally Determne the Number of Clusters Found by a Local Network Neghbourhood Artfcal Immune System. Appled Soft Computng Journal, 20. [3] M. Bereta. Immune K-means and Negatve Selecton Algorthms for Data Analyss. Informaton Scences, [4] S. Gou. Mult-eltst Immune Clonal Quantum Clusterng Algorthm. Neurocomputng, 203. [5] R. Lu. Immunodomance Based Clonal Selecton Clusterng Algorthm. Appled Soft Computng, 202. [6] J. Wang. Study of Optmzng Method for Algorthm of Mnmum Convex Closure Buldng for 2D Spatal Data. Acta Geodaetca et Cartographca Snca,