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dc.contributor.authorSubudhi, B-
dc.contributor.authorJena, D-
dc.identifier.citationIEEE TENCON 2009, 23-26 November 2009, Singaporeen
dc.descriptioncopyright belongs to TENCONen
dc.description.abstractThis work presents system identification using neural network approaches for modelling a laboratory based twin rotor multi-input multi-output system (TRMS). Here we focus on a memetic algorithm based approach for training the multilayer perceptron neural network (NN) applied to nonlinear system identification. In the proposed system identification scheme, we have exploited three global search methods namely genetic algorithm (GA), Particle Swarm Optimization (PSO) and differential evolution (DE) which have been hybridized with the gradient descent method i.e. the back propagation (BP) algorithm to overcome the slow convergence of the evolving neural networks (EANN). The local search BP algorithm is used as an operator for GA, PSO and DE. These algorithms have been tested on a laboratory based TRMS for nonlinear system identification to prove their efficacy.en
dc.format.extent252853 bytes-
dc.subjectDifferential evolutionen
dc.subjectEvolutionary computationen
dc.subjectNonlinear system identificationen
dc.subjectBack propagationen
dc.subjectTwin rotor systemen
dc.titleNonlinear System Identification of A Twin Rotor MIMO Systemen
Appears in Collections:Conference Papers

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