Automatic generation of pragmatic and intelligible fuzzy rules

Rule-based systems have been the forerunner among contemporary techniques for systems engineering. A doyen of the intelligent systems, they have undergone philosophical and methodological transformations and have proven to be very successful in applications such as robotics, geometric information systems, medical instrumentation, image processing, natural language processing and interactive systems. However, due to the knowledge acquisition bottleneck, the development of rule-based systems is time-consuming, carries high costs, and has high risks. In this paper, we propose a methodology that aims at overcoming the bottleneck by automatically generating intelligible and robust rules based on empirical data. With statistics as its foundation and bolstered by heuristic search, the, proposed methodology has been successful in cutting through the maze of complex systems in generating more than a plausible solution. This was confirmed by real-world engineering applications. Techniques that can automatically generate and refine fuzzy rules do exist in the literature. A popular method is the adaptive neural-fuzzy inference system (ANFIS) that refines fuzzy rules obtained using clustering techniques. However, ANFIS utilizes the Sugeno type fuzzy rules. The rules generated are not easy to comprehend, since the consequents are a crisp functions rather than fuzzy linguistic terms. Moreover, the approach cannot deal with discrete outputs. Our methodology generates the more popular, versatile, and easily understood Mamdani type fuzzy rules. Neural network-type learning algorithms are then used to optimize parameters of fuzzy membership functions and relieve the defuzzification impediment