Fuzzy interpolation systems with knowledge extraction and adaptation

Fuzzy inference system provides an effective means for representing and processing vagueness and imprecision. Conventional fuzzy modelling requires either complete experts’ knowledge or given datasets to generate rule bases such that the input spaces can be fully covered. Although fuzzy interpolation enhances the power of conventional fuzzy inference approaches by addressing the problem of lack of knowledge represented in the rule bases, it is still difficult for real-world applications to obtain sufficient experts’ knowledge and/or data to generate a sparse rule base to support fuzzy interpolation. Also, the generated rule bases are usually fixed and therefore cannot support dynamic situations. In addition, all existing fuzzy interpolation approaches were developed based on the Mamdani fuzzy model, which are not applicable for the TSK fuzzy model. It significantly restricts the applicability of the TSK fuzzy inference systems. This PhD work, in the first part, presents a novel fuzzy inference approach, termed “TSK+ fuzzy inference”, to address the issue of performing the TSK inference over sparse rule bases. The proposed TSK+ fuzzy inference approach extends the conventional TSK fuzzy inference by considering the degree of similarity between given inputs and corresponding rule antecedents instead of conventional overlapped match degree, which allows TSK inference to be performed over sparse rule bases, dense rule bases, and imbalanced rule bases. In order to support the proposed TSK+ inference approach, a data-driven rule base generation method is also presented in this work. In addition, the proposed TSK+ inference approach has been further extended to deal with interval type-2 fuzzy sets. The effectiveness of this system in enhancing the TSK fuzzy inference is demonstrated through two real-world applications: a network intrusion detection system, and a network quality of service management system. In addition, in the second part of this work, a new rule base generation and adaptation method is developed in order to relax the requirement of rule base generation, which allows the fuzzy rule base to be generated with minimal or even without a priori knowledge. The proposed method mimics the pedagogic approach of experiential learning, which achieves automatic rule base generation and adaptation by transferring the proceeding performance experiences when performing inferences. The proposed rule base generation and adaptation method has been evaluated by not only a mathematical model but also a well-known control problem, inverted pendulum. The experimental results show that the control system can generate an applicable rule base to support the system running, thus demonstrating the effectiveness of the proposed approach.