ASAFES: adaptive stochastic algorithm for fuzzy computing/function estimation

摘要

Presents ASAFES, a novel architecture for fuzzy computing, featuring a different approach to the function approximation problem, ASAFES is a reinforcement learning algorithm which is able to learn a multivariable function online, from examples, and create the set of fuzzy rules and their corresponding weights (significances) which expresses the function, without requiring prior knowledge of the exact output value for each combination of input values. It can be initialized with explicit human knowledge or start from complete ignorance, can learn from noisy data, generalise, automatically adapt on the way if needed, using just a reinforcement signal which approximately indicates how correct was its output at every iteration. The authors' scheme employs a stochastic search for the right consequence and corresponding weight for each possible fuzzy rule, using the stochastic estimator learning algorithm and regression analysis. It is a fuzzy computer, integrating neural networks advantages, and fuzzy logic appeal.