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" In the present invention, prior art techniques are extended to allow application of the backpropagation learning technique to artificial neural networks derived from fuzzy expert system rule-bases. A method in accordance with the invention, referred to herein as a Fuzzy Expert Network (FEN), is implemented in a programmed machine such as a computer to provide automated learning of both ""fine"" and ""coarse"" knowledge in a network of artificial neural objects (ANOs) implementing fuzzy modeling rules. Through application of the FEN method, an event-driven fuzzy expert network comprising acyclically connected ANOs derived from fuzzy modelling rules may be implemented. Neural objects implement one or more fuzzy combining and defuzzification rules and use backpropagation of error techniques to implement learning. As in prior art, the FEN allows each ANO to adjust its input weight parameters--""fine"" knowledge learning. Unlike prior art, the FEN allows each ANO to modify its internal parameters--""coarse"" knowledge learning. This latter action means that individual ANOs have the capability to modify the parameters of the fuzzy rule's membership function upon which they are based. In this way the FEN is able to change the structure of its encoded knowledge over time, making it a more adaptable architecture for autonomous and/or adaptable control systems. Simulation results showing the FEN's learning and adaptability behavior are given. "
