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Few technologies have been used for such a vast variety of applications as neural networks and fuzzy systems. They have been found to be truely interdisciplinary tools appearing in the fields of economics, business, science, psychology, biology, and engineering to name a few. Based upon the structure of a biological nervous system, artificial neural networks use a number of interconnected simple processing elements (“neurons”) to accomplish complicated classification and function approximation tasks | Stable Adaptive Control and Estimation for Nonlinear Systems Neural and Fuzzy Approximator Techniques. Jeffrey T. Spooner Manfredi Maggiore Raul Ordonez Kevin M. Passino Copyright 2002 John Wiley Sons Inc. ISBNs 0-471-41546-4 Hardback 0-471-22113-9 Electronic . 1 o rici 3L nr p Jf Neural Networks and Fuzzy Systems Overview Few technologies have been used for such a vast variety of applications as neural networks and fuzzy systems. They have been found to be truely interdisciplinary tools appearing in the fields of economics business science psychology biology and engineering to name a few. Based upon the structure of a biological nervous system artificial neural networks use a number of interconnected simple processing elements neurons to accomplish complicated classification and function approximation tasks. The ability to adjust the network parameters weights and biases makes it possible to learn information about a process from data whether it is describing stock trends or the relation between an actuator input and some sensor data. Neural networks typically have the desirable feature that little knowledge about a process is required to sucessfully apply a network to the problem at hand although if some domain-specific knowledge is known then it can be beneficial to use it . In other words they are typically regarded as a black box technique. This approach often leads to engineering solutions in a relatively short amount of time since expensive system models required by many conventional approaches are not needed. Of course however sufficient data is typically needed for effective solutions. Fuzzy systems are intended to model higher level cognitive functions in a human. They are normally broken into 1 a rule-base that holds a human s knowledge about a specific application domain 2 an inference mechanism that specifies how to reason over the rule-base 3 fuzzification which transforms incoming information into a form that can be used by the fuzzy system and 4