Training fuzzy logic based software components by combining adaptation algorithms

Abstract

 A training framework of an effective method for off-line training of a class of control software components (e.g., 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 a faulty FL rule-based software component for the tracker problem. In the framework, training consists of two phases: testing and adapting. In the testing phase, a test driver generates an effective fault scenario ( fs) and locates the faulty fuzzy elements (FFEs) by using each or a combination of three adaptation algorithms. In the adapting phase, for each fault scenario adaptation algorithms and their combinations are used to modify the MBFs of the component. Effectiveness of the two phase training is determined in terms of testability, flexibility, adaptability, and stability. An initial design of the simulation environment is presented. In the experiment, for a given circumstance (environment and fuzzy rules) we apply a combination of a genetic algorithm GA) and a neural network (NN) with an error back-propagation algorithm (BP) in the testing phase for generating fault scenarios. Then we apply GA-only method in the adapting phase for adapting the faulty software component. Simulation results on effectiveness and efficiency are discussed.