Finite Difference approach on RBF networks for on-line system identification with lost packet

Andryani, N.A.C. and Asirvadam, V.S. and Hamid, N.H. (2009) Finite Difference approach on RBF networks for on-line system identification with lost packet. In: Electrical Engineering and Informatics, 2009. ICEEI '09. International Conference on, 5-7 Aug 2009, Bangi, Selangor, Malaysia.

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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 especially in identifying a black box system. In many cases, identifying of the system process normally has lack of data or may lose some packets data needed in the identifying process. Finite Difference approach with its enhancement, Richardson Extrapolation, is used to improve the learning performance especially in the non linear learning parameter update for identifying system with lost packet data case in online manner. Since initializing of non linear learning's parameters is crucial in RBF networks' learning, random initialization is placed with some clustering method. Some unsupervised learning methods such as, K means clustering and Fuzzy K means clustering are used to replace it. All the possible combination methods in the initialization and update process try to improve the whole performance of the learning process regarding to the system identification with lost packet data case. It can be showed that Finite difference approach with dynamic step size on recursive prediction error for the non linear parameter update with appropriate initialization method succeed to perform better performance compared to extreme learning machine (ELM) as the previous learning method.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:RBF network;Richardson extrapolation;black box system;clustering method;extreme learning machine;feed forward neural network architecture;finite difference approach;multi layer preceptor;nonlinear learning parameter;online system identification;radial basis function network;recursive prediction error;unsupervised learning method;extrapolation;finite difference methods;identification;multilayer perceptrons;pattern clustering;radial basis function networks;unsupervised learning;
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Centre of Excellence > Center for Intelligent Signal and Imaging Research
ID Code:4643
Deposited By: Dr Vijanth Sagayan Asirvadam
Deposited On:21 Nov 2011 06:29
Last Modified:19 Jan 2017 08:25

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