Finite difference recursive update on decomposed RBF networks for system identification with lost packet

Andryani, N.A.C. and Asirvadam, V.S. and Hamid, N.H. (2009) Finite difference recursive update on decomposed RBF networks for system identification with lost packet. In: UNSPECIFIED.

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Abstract

Radial Basis Function networks (RBF) is one form of feed forward neural network architecture which is popular besides Multi Layer Preceptor (MLP). It is widely used in identifying a black box system. Finite Difference approach is used to improve the learning performance especially in the non-linear learning parameter update for identifying system with lost packet in online manner. Since initializing of non-linear learning's parameters is crucial in RBF networks' learning, some unsupervised learning methods such as, K-means clustering and Fuzzy C-means clustering are used besides random initialization. All the possible combination methods in the initialization and updating process try to improve the whole performance of the learning process in system identification with lost packet compared to Extreme Learning Machine as the latest improved learning method in RBF network. It can be shown that Finite difference approach with dynamic step size on Decomposed RBF network with Recursive Prediction Error for the non-linear parameter update with appropriate initialization method succeed to perform better performance compared to ELM. © 2009 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 1; Conference of International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 ; Conference Date: 4 December 2009 Through 7 December 2009; Conference Code:79459
Uncontrolled Keywords: Black box system; Combination method; Extreme learning machine; Finite difference; Finite difference approach; Fuzzy C means clustering; Identifying system; Initialization methods; K-means clustering; Learning methods; Learning performance; Learning process; Multi-layer preceptors; Non-linear parameters; Nonlinear learning; One-form; Online learning; RBF Network; Recursive prediction; Recursive update; Step size; System identifications; Unsupervised learning method, Identification (control systems); Neural networks; Pattern recognition; Soft computing; Unsupervised learning, Radial basis function networks
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:48
Last Modified: 09 Nov 2023 15:48
URI: https://khub.utp.edu.my/scholars/id/eprint/607

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