RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION

• Main Author: CATUR ANDRYANI, NUR AFNY
• Format: Thesis
• Language: English; English
• Published: 2010
• Online Access: http://utpedia.utp.edu.my/2907/1/PREFATORY_PAGES_-_AFNY.pdf; http://utpedia.utp.edu.my/2907/2/All_Main_Chapters-Afny.pdf; http://utpedia.utp.edu.my/2907/

Science and technology development has the tendency of learning from nature where human also try to develop artificial intelligent by imitating biological neuron network which is popularly termed Artificial Neural Network (ANN). It represents an interconnection among neuron which consists of several adjustable parameters which are tuned using a set of learning examples to obtain the desired function represent the actual system. Radial Basis Function (RBF) networks, one of feed forward artificial neural network architecture, have recently been given much attention due to its good generalization ability. The RBF network is popular among scientist and engineer and used in a number of wide ranging signal and control applications which includes the area of system identification or estimation. The learning approach, a process which updated the parameters of RBF networks, will be the most important issue in neural computing research communities. The learning method will determine the performance’s capability of the networks for the system identification process which will be one of the key issues to be discussed in the thesis. This thesis proposes derivative free learning, using finite difference, methods for fixed size RBF network in comparison to gradient based learning for the application of system identification. The thesis also try to investigate the influence of initialization of RBF weights parameters on the overall learning performance using random method and advanced unsupervised learning, such as clustering techniques, as a comparison. By taking advantage of localized Gaussian basis function of RBF network, a decomposed version of learning method using finite difference (or derivative free) gradient estimate has been proposed in order to reduce memory requirement for the computation of the weight updates. The proposed training algorithms discussed in this thesis are derived for fixed size RBF network and being compared with Extreme Learning Machine (ELM) as the ELM technique just randomly assigned centers and width of the hidden neurons and update the output connected weights. The proposed methods are tested using well known nonlinear benchmark problems and also evaluated for system with irregular sample time or known as lost packets. The finite difference based gradient estimate, proposed in this thesis, provides a viable solution only for identifying a system with irregular sample time.