Method for optimization of a fuzzy neural network

摘要

Optimization of a FNN (FNN)-based controller is described. The optimization includes selecting which input signals will be used by the FNN to compute a desired control output. Output parameters are identified and computed by fuzzy reasoning using a neural network. Adjustment of fuzzy rules and/or membership functions for the FNN is provided by a learning process. The learning process includes selecting candidate input data signals (e.g. selecting candidate sensor signals) as inputs for the FNN. The input data is categorized and coded into a chromosome structure for use by a genetic algorithm. The genetic algorithm is used to select an optimum chromosome (individual). The optimum chromosome specifies the number(s) and type(s) of input data signals for the FNN so as to optimize the operation of the FNN-based control system. The optimized FNN-based control system can be used in many control environments, including control of an internal combustion engine.