Research on Neural Network Model Based on Subtraction Clustering and Its Applications

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1 Avalable onlne at Physs Proeda 5 (01 ) Internatonal Conferene on Sold State Deves and Materals Sene Researh on Neural Networ Model Based on Subtraton Clusterng and Its Applatons ZHANG Te-jun a, CHEN Duo a,sun Je b a Department of Computer Sene and Tehnology,Tangshan College,Tangshan, Hebe Provne, Chna b Shool of Computer and Control, Hebe Polytehn Unversty, Tangshan, Hebe Provne, Chna Abstrat Ths paper taes advantage of the ntegraton of subtraton lusterng and fuzzy -means lusterng algorthm to obtan prese number of lusters and the degree of membershp, and establshes the neural networ model based on the multple rteron nformaton fuson and the fuzzy tehnology. Colletng a varety of data n atual underground mnng proess and ntegratng them as a whole, the smulatons are performed. Expermental results ndate that the proposed method s vald Publshed by Elsever Ltd. B.V. Seleton and/or peer-revew under responsblty of of [name Garry organzer] Lee Open aess under CC BY-NC-ND lense. Keywords : subtratve lusterng; fuzzy -means lusterng; fuzzy neural networ 1.Introduton In fat, most of the systems of atual produton proess an not be modeled usng an exstng model; aordngly, to solve ths problem s qute dffult. The neural networ does not rely on the aurate model and has a seres of advantages of parallel omputng, dstrbuted nformaton storage and adaptvely learnng funton and son on. It s just beause of these advantages, the neural networ tehnology has been gradually appled to many ndustral proesses. However, n more omplex ndustral proesses, whh onsst of the quantty of data and multple dmensons, f the neural networ s dretly used, we mght have the problem of yeldng naurate results and not beng onvergene n ther tranng phase. Amed at these shortomngs, the neural networ model based on subtraton lusterng and the fuzzy -means s presented n ths paper, n whh, the fuzzy lusterng s run two tmes to deal wth the ntal omplex data, and then the neural networ are utlzed for further operaton Publshed by Elsever B.V. Seleton and/or peer-revew under responsblty of Garry Lee Open aess under CC BY-NC-ND lense. do: /j.phpro

2 Zhang Te-jun et al. / Physs Proeda 5 ( 01 ) Data lusterng proess Clusterng s a proess of lassfaton when only data avalable are unlabeled and no pror nowledge about t [1]. Aordng to a defnton of metr of smlarty, a set of objets are parttoned nto a ertan number of lusters, so that objets n the same lusters are as smlar as possble and objets n dfferent lusters are as dssmlar as possble n the sense of the defnton..1 Subtratve lusterng In prate, the struture of data dstrbuton annot be usually nown n advane, but n lusterng method some parameters, suh as the number of luster, s requred to be gven. In order to ondut more aurately the nherent haraterst of tranng data, the subtratve lusterng s utlzed to obtan the ntal luster enters of the fuzzy -means algorthm n ths paper, n whh the subtratve lusterng algorthm s based on the mountan funton. By the establshment of mountan funton that s an ndex of data densty, subtratve lusterng algorthm an adaptvely determne the number of lusters and luster enter tself, furthermore the ntal struture of the objetve system. The subtratve lusterng s a nd of smple and effetve algorthm based on the formng prnple of human vsual data set and the orrelaton between data. Let the number of m-dmenson nput data set X s equal to n, sne eah data pont s a anddate for luster enters, the mountan funton that expresses the densty of X s frst onstruted as follows: n x x 1 j m ( x ) exp (1) a / Where, s a postve number, t expresses luster radus and defnes a neghborhood for the pont. If there are larger amount of data ponts near a data pont, then the data wll has a hgher pea, whle data ponts outsde the radus has lttle ontrbuton to the pont densty of the pont. After alulatng the densty of all ponts, the pont wth the maxmum densty s seleted as the frst luster enter x l, where, l m s ts orrespondng ndex value of densty. To ompute the next luster enter, t s requred to elmnate the mpat of the exstng luster enter; therefore the densty ndex of mountan funton s modfed as follows: 1 m ( x ) m 1 ( x ) m 1 exp x x 1 / where, expresses the neghbourhood that the densty ndex s sgnfantly redued, and to avodng yeldng very near luster entres, we have =1.5.Mountan funton value wll be pared to selet a new luster entres, when the new luster enter to meet the approprate densty ndex m / ml, stop searhng the new luster enter. The ntal ondton of the fuzzy -means lusterng algorthm s the luster entres omputed by the subtratve lusterng n advane, therefore avodng yeldng the error by assgned arbtrarly the number of luster, when only usng the fuzzy -means algorthm to obtan the membershp degree of data set.. The Fuzzy C-means lusterng (FCM) Sne most atual objets are not strtly property, there s the ntermedary nature n ther property and gener,.e. they have the nature of both ths and that. The degree of samples belongng to varous lusters an be determned by the fuzzy lusterng, whh expresses the ntermedary of the sample and well reflets the real world objetvely. ()

3 1644 Zhang Te-jun et al. / Physs Proeda 5 ( 01 ) The vetor X j (j =1,,,n) s parttoned nto lusters G ( =1,,,), the lusterng proess s desrbed as follows: Intalzaton: undertae the luster enters and the number of lusters, obtaned by the method n seton.1, set teraton threshold, ntalze the luster prototype P (0), and the teraton ounter b=0; Step 1: alulate or update partton matrx U (b) ( ) :d j = - x j,,, f d b 0, we have: Step : update luster prototype matrx P(b+1) ( b) ( b1) m ( ) x ( b1) 1 p, 1,, n ( b1) m ( ) 1 (4) Step 3: f p(b) -p(b+1) <, then stop and output partton matrx U and luster prototype P, else let b=b+1, go to the Step 1. The hoe of the weghed ndex m plays an mportant role n adjustng fuzzy degree of lusterng, and m= n ths paper. Through FCM the fuzzy partton matrx and luster prototype an be obtaned, and then the membershp degree of roof stablty on dfferent nfluene fators. 3.Fuzzy neural networs wth nformaton fuson Informaton Fuson, also nown as mult-sensor nformaton fuson tehnology, or data fuson tehnology, maes ntegraton and fuson of the data that ome from multple sensors by mtatng experts' apaty of omprehensvely proessng nformaton, so obtanng more aurate and redble onlusons than usng eah sensor respetvely []. Artfal neural networ s establshed based on a type of mro-struture and funton that s the smulaton of the human neural system, wth some smulated ablty of thnng n mages. The nformaton fuson has strong smlartes ompared wth neural networs n struture, just beause the nformaton fuson s based on ntellgent thought, and ts funton to be aheved s to mtate the apablty of human bran to deal wth all nds of nformaton, whh s very lose to the dea of neural networ. Havng the smlarty wth neural networs n struture, ths paper taes full advantage of ther superorty and deals wth the relatonshp and nteraton among nformaton proessng unts, onsequently, usng the fve-layer fuzzy neural networ shown n Fg. 1. The frst layer: nput xj(j =1,,,n) dretly, where n s the number of nput varable. d d n ( b) ( b) j m1 1 (3) Fg. 1 The struture of fuzzy neural networ The seond layer: fuzzy membershp funton layer, atvaton funton s the membershp funton on a fuzzy subset, whh represents the nput varable membershp. Eah unt of the layer s atually a small neural networ, as shown n Fg..

4 Zhang Te-jun et al. / Physs Proeda 5 ( 01 ) j j j exp (5) j ( x ) Fg. The sub-neural networ of nformaton fuson The thrd layer, eah node represents a fuzzy rule, these nodes ondutng "and" operaton to ntegrate the values nputted n the seond layer, furthermore mathng the anteedent of fuzzy relatons and alulatng the degree of applaton of eah rule. The fourth level, the number of nodes s stll m, alulate to aheve normalzaton. n ~ 4 4 y ( ) j ( ) ( ) 1 (6) ~ 4 j The ffth layer,.e. output layer, deblurrng by the weghted sum method. Where, s the onneton weght between th node of fuzzy rule layer and jth node of output layer, s also atvated ntensty of jth output assoated wth th rule, whh an be adjusted. [3]. 4.Parameter tranng The parameters to be undertaen are v j, j and w, traned by BP algorthm. Amng the drawbas of standard BP algorthm, suh as slow onvergene rate, beng easy to form a loal mnmum, et. ths paper adopts an mproved method, n whh the atvaton funton are modfed as follows: 1 1 f ( x) 1 exp( x) (7) The varable-rate learnng method s desgned, n whh, when the networ tranng beng the ntal stage, learnng rate s assgned a larger onstant to nrease the learnng speed; whle the output error has been small enough, thes redued to derease ts speed. In ths way, the auray of onvergene s ensured and the tranng preson an be mproved. In ths paper the weght adjustng funton wth momentum term s utlzed as follows: a( 1) a( ) a ( ) (8) 1 y ) y ( (9) where, a() s urrent value of membershp funton parameters and a(+1) s ts modfed values, s the ontrol parameter of speed orreton, s the momentum oeffent, s the momentum oeffent, s the error objetve funton.

5 1646 Zhang Te-jun et al. / Physs Proeda 5 ( 01 ) Smulaton The above mentoned model s appled to oal mnng proess wth a speal omplex produton ondtons, so that the predton s arred out through the neural networ. The eght man fators related losely to the roof stablty are onduted to arry out the smulaton tranng, they are mnng depth, roof ro feature, struture omplexty, angle of oal seam, oal seam thness, mnng method, wth or wthout pllar and mnng mode, shown as Table 1. Table 1 The expermental sample Sample mnng depth roof ro feature oal seam thness angle of oal seam wth or wthout pllar 1 51 sandstone No 75 sandstone.0 48 No mudstone Yes sandstone No mudstone Yes mudstone Yes sandstone Yes 5.1 Parameter Seleton The membershp degree s used as the networ nput, whh s obtaned by lusterng the expermental sample at two tmes. Aordng to the "Roof lassfaton sheme for gently nlned oal fae" suggested by Mnstry of Coal Industry of Chna, the stablty of oal mne roof dvded nto 4 grades: unstable roof, Medum stable roof, stable roof and strong roof. Therefore, the number of output nodes s 4, orrespondng to the 4 grades respetvely. The number of luster s 5 by the herarhal lusterng. In the fuzzy neural networ n ths paper, the frst layer has 8 nodes, whh s the same as the dmenson; the seond layer s the membershp funton layer and has 40 nodes; The thrd layer s fuzzy nferene layer, has 5 rules that are obtaned by the fuzzy means lusterng, thus the layer nodes s also equal to 5; the fourth layer s the de-fuzzy and output layer, has 4 nodes that s orrespond to the 4 nds of stablty. Therefore, the neural networ has the ( ) struture. Through smulaton, the ntal values of =0.015 and =0.7, of whh, the value of vares wth tranng proess. 5. Results In order to avod overflowng, the samples of mnng depth, roof ro feature and oal seam thness are normalzed at frst, as shown n Table.The dsretzaton proessng s made for the other samples, that s, 0 and 1 are used to represent dfferent states, for example, mudstone s expressed as 1 and sandstone as 0, wth oal pllar s expressed as 0 and wthout oal pllar as 1, et. After normalzaton proess, the samples are parttoned nto 5 lusters by the herarhal lusterng algorthm based on feld potental topology, and then the fuzzy -means lusterng s run and the results are shown as Fg. 3. Table The samples after normalzaton Sample mnng depth angle of oal oal seam seam thness

6 Zhang Te-jun et al. / Physs Proeda 5 ( 01 ) Fg. 3 FCM lusterng results Fg. 4 The networ error urve The fuzzy neural networ's parameters derved from lusterng results are traned by the self-adaptve learnng rate so that the algorthm results onverge faster and have less error shown n Fg. 4.The results show that the traned fuzzy neural networ showed better fuson effet, whh reeves more nformaton and optmzed parameters. The predton system that the fuzzy neural networ s adopted to fuse data s superor to the sngle rteron system. 6.Conlusons Amng at the speal nature of oal mne roof stablty, ths paper ollets a varety of data and ntegrate them as a whole, and has arred out the predton usng the fuzzy neural networ based on the nformaton fuson after the subtratve lusterng and fuzzy -means lusterng. Traned neural networ model has satsfed the gven demand and an be appled to the smulaton predton. Meanwhle the baground database an be used to store data so that the performane of the networ model an be mproved and ts predtablty s enhaned ontnuously. So the system has the real-tme property and better usablty. Referenes [1] GAO Xn-bo, Fuzzy Cluster Analyss and ts Applatons [M], X'an: Xdan Unversty Press, 004: [] SUN Zheng-yn, ZHANG Za-xng. Intellgent Control Theory and Tehnology [M]. Bejng: Tsnghua Unversty Press,000: [3] YUAN Zeng-ren. Artfal Neural Networ and Its Applatons [M]. Bejng: Tsnghua Unversty Press, 1999.