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Title: Nonlinear System Identification of A Twin Rotor MIMO System
Authors: Subudhi, B
Jena, D
Keywords: Differential evolution
Evolutionary computation
Nonlinear system identification
Back propagation
Twin rotor system
Issue Date: 2009
Publisher: IEEE
Citation: IEEE TENCON 2009, 23-26 November 2009, Singapore
Abstract: This 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.
Description: copyright belongs to TENCON
Appears in Collections:Conference Papers

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