An adaptive wavelet-network model for forecasting daily total solar-radiation

Recently, through the use of parameterized fuzzy approximators, various adaptive fuzzy control schemes have been developed to deal with nonlinear systems whose dynamics are poorly understood. An important class of parameterized fuzzy approximators is constructed using radial basis function (RBF) as a membership function. However, some tuneable parameters in RBF appear nonlinearly and the determination of the adaptive law for such parameters is a nontrivial task. In this paper, we propose a new adaptive control method in an effort to tune all the RBF parameters thereby reducing the approximation error and improving control performance. Global boundedness of the overall adaptive system and tracking to within a desired precision are established with the new adaptive controller. Simulations performed on a simple nonlinear system illustrate the approach.

Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators

An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria

(ABSTRACT) Single layer feedforward neural networks with hidden nodes of adaptive wavelet functions (wavenets) have been successfully demonstrated to have potential in many applications. Yet applications in the process control area have not been investigated. In this paper an application to a self-tuning design method for an unknown nonlinear system is presented. Different types of frame wavelet functions are integrated for their simplicity, availability, and capability of constructing adaptive controllers. Infinite impulse response (IIR) recurrent structures are combined in cascade to the network to provide a double local structure resulting in improved speed of learning. In particular, neuro-based controllers assume a certain model structure to approximate the system dynamics of the " unknown " plant and generate the control signal. The capability of neuro-controllers to self-tuning of an unknown nonlinear plants is then illustrated through design examples. Simulation results demonstrate that the self-tuning design methods are directly applicable for a large class of nonlinear control systems. valuable time to serve on my Ph.D. advisory committee. Their attentive responses and suggestions toward my dissertation are sincerely appreciated. Besieris whose reviews and constructive criticisms have improved the quality of this work. My sincere thanks are due to them for taking time from their busy schedules to serve on my advisory committee. Finally, this research can never be completed without the continuous support, love, and encouragement of my parents. This dissertation is entirely dedicated to them. My very special thanks to my beloved fiancee, Puntipa Yanothai, for her love, patience, support, and unfailing enthusiasm in keeping continuity of this research.

Adaptive Self-Tuning Neuro Wavelet Network Controllers

Sizing of stand-alone photovoltaic systems using neural network adaptive model

This paper presents a local neural network structure called spatiotemporally local network, by combining the radial basis function network (RBFN) and the local recurrent networks. Three local structures are proposed and the algorithms are compared for nonlinear dynamic system identification. System dynamics can be fully modeled with the fast learning of the proposed neural network structure.

Dynamic System Identification using Recurrent Radial Basis Function Network

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Dynamic System Identification using Recurrent Radial Basis Function Network