Abstract
This paper presents the dynamic neuro-fuzzy control system for controlling a gas-fired water heater. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic neural networks in the forward path. A dynamic identification network (DIN) is used to identify the output of the manipulator system, and a dynamic learning network (DLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the gas-fired water heater system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input-output relationship using a dynamic back propagation learning algorithm.
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