FUZZY MESSAGE REQUIREMENT CLASSIFIERS SYSTEM AND AN APPLICATION TO THE IC TEST MACHINE DIAGNOSTICS-I

Abstract

ABSTRACT In modern complex environment with continuous updating information, knowledge acquisition has become a bottleneck for the expert systems. A symbolic artificial intelligence (AI) system may possess reasoning knowledge with interpretability, but lack of learning capability, especially when environment status and dynamic trend change, and habits of input condition switch. Numerical artificial intelligence systems, on the other hand, possess relearning capability, but lack of interpretability, such as a bunch of hard intuitive and hard recognizing weights for representing knowledge. Therefore, integrating the advantages of the two types of systems can be an important issue in current and future research. Holland first proposed the classifiers system in 1975. The classifiers systems are genetic-algorithm based, incorporate the reinforcement learning, and constitute one of the theories of machine learning. It uses simple binary-variables (1, 0, #) for knowledge representation and expedites the system's responsive speed. Nevertheless, there is a limitation if it encountered with environmental uncertainty or fuzziness, continuous input variables and fuzzy feedback. Based on these demands described above, this paper proposes a fuzzy message requirement classifiers system (FMRCS), which integrates learning and reasoning. The FMRCS consists of two systems: the message requirement classifiers system (MRCS) and the fuzzy classifiers system (FCS). First, the MRCS simulates the internal reasoning process of human being and finds the key factors of decision problem under limited known messages in the problem domain. Then, according to the key factors created by the MRCS, the FCS learns the relationships between all factors concerned of the problem domain and various results of this problem.