Paper Title

Classical control theory is based on the mathematical models that describe the physical plant un-der consideration. The essence of fuzzy control is to build a model of human expert who is capa-ble of controlling the plant without thinking in terms of mathematical model. The transformation of expert's knowledge in terms of control rules to fuzzy frame work has not been formalized and arbitrary choices concerning, for example, the shape of membership functions have to be made. The quality of fuzzy controller can be drastically affected by the choice of membership functions. Thus, methods for tuning the fuzzy logic controllers are needed. In this paper, neural networks are used in a novel way to solve the problem of tuning a fuzzy logic controller. The neuro fuzzy controller uses the neural network learning techniques to tune the member-ship functions while keeping the semantics of the fuzzy logic controller intact. Both the architec-ture and the learning algorithm are presented for a general neuro fuzzy controller. From this gen-eral neuro fuzzy controller, a proportional neuro fuzzy controllers is derived. A step by step algo-rithm for off-line training is given along with numerical examples.