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Document Type : Latin Dissertation
Language of Document : English
Record Number : 151376
Doc. No : ET23168
Main Entry : SYED SAAD AZHAR ALI
Title Proper : - - - - - IDENTIFICATION OF HAMMERSTEIN AND WIENER MODELS USING RADIAL BASIS FUNCTIONS NEURAL NETWORKS
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : A new method 2s introduced for the identification o/ particular tgpes of nonlinearsystems ie., Hammerstein and Wiener models. The Hammerstein model consdts ofa static nonlinearity. followed by a linear dynaml'c block. while the Wiener model con-sists of the linear dynamic block followed by the static nonlzneant y. The nonlinearit yand the linear dynamic part in both the models are identzfied by trsing radial h i sjunctions neural network (RBFNN) and nutoregressive moving average (.4RbI14)model. respectiuelg. The ne,w algorithm makes use of the well known mapping abilityof RBFNN. Learning algorithms based on least mean squares (LAIS) principle arederived for the training of the identification sclterne. The proposed algorithms esti-mate the weights of the R BFNN and the....-....,....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject : Electericl tess
: برق
electronic file name : TL46404.pdf
Title and statement of responsibility and : - - - - - IDENTIFICATION OF HAMMERSTEIN AND WIENER MODELS USING RADIAL BASIS FUNCTIONS NEURAL NETWORKS [Thesis]
 
 
 
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