Training fuzzy logic based software components for reuse

A training framework of an effective method for off-line training of a class of control software components (for example, for first-order nonlinear feedback control systems) using combinations of three kinds of adaptation algorithms is presented. Each control software component is represented at the abstract level by means of a set of adaptive fuzzy logic (FL) rules and at the concrete level by means of fuzzy membership functions (MBFs). At the concrete representation level adaptation algorithms specified for use in adapting MBFs are: genetic algorithms, neural net algorithms, and Monte Carlo algorithms. We specify effective combinations of these three existing adaptation algorithms to train an erroneous FL rule-based software component in the tracker problem. In the framework, training consists of two phases: testing and adapting. In this paper, only the adapting phase is addressed. For each fault scenario adaptation algorithms and their combinations are used to modify the MBFs of the component. Effectiveness of the adapting phase is determined in terms of flexibility, adaptability, and stability. We perform experiments using a genetic algorithm. Simulation results are discussed