PATTERNCLASSIFICATION USINGLOCALLINEARWAVELET NEURAL NETWORK

alternative approach tononlinear fitting problem Inthis paper wehaveusedaLocal Linear Wavelet[1,2,3,10,11]. Twokeyproblems indesigning the NeuralNetwork(LLWNN)modelforpattern WNN arehowtodetermine WNN architecture and classification. Thedifference ofthenetwork withwhatlearning algorithm canbeeffectively usedfor conventional wavelet neural network (WNN)isthattraining theWNN [4]. Theseproblems arerelated to theconnection weights between thehidden layer and determine anoptimal WNN architecture, toarrange output layer ofconventional WNN arereplaced bya thewindows ofwavelets, andtofind theproper lou t ..t S Optimisationorthogonal ornonorthogonal wavelet basis. Curse of local linear modlel. Particle SwarmOptimisation diesonlt isa manl unovdpolmi (PSO)technique usedfortraining theLLWNN. dmensionality isa mamly unsolved problem in Simulation results fortheclassification ofdifferent WNN theory, which brings somedifficulties in benchmark datasets showthefeasibility and applying aWNN tohigh dimension problems. effectiveness oftheproposedmethod. Thebasis function neural networks areaclass of neural networks, inwhichtheoutput ofthenetwork