Abstract
Setting fuzzy rules is one of the paramount techniques in the design of a fuzzy system. For a simple system, fuzzy if-then rules are usually derived from the human experts. However, in the event of having multiple variables coupled with a few features, the classification problem will be getting more sophisticated, as a result human expert may not be able to derive proper rules. This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices without detailed computation (hereafter denoted as GA-FIS). The impetus for developing a new and efficient GA-FIS model arises from the need of constructing fuzzy rules directly from raw data sets that combines good approximation and classification properties with compactness and transparency. Therefore, our proposed GA-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability. The proposed approach is applied to various benchmark and real world problems and the results show its validity.
Highlights
Fuzzy Inference System (FIS) has been an emerging practical solution for successfully solving numerous kind of process control and instrumentation problems [1,2,3,4,5,6,7,8,9,10]
This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices wit hout detailed computation
Our proposed Genetic Algorithm (GA)-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability
Summary
Fuzzy Inference System (FIS) has been an emerging practical solution for successfully solving numerous kind of process control and instrumentation problems [1,2,3,4,5,6,7,8,9,10]. Ways to generate fuzzy if rules automatically from the source of raw data has been widely researched over the years [1621] When it comes to high-dimensional problems, there will be an exponential increase of the number of fuzzy if- rules with the number of inputs, i.e., the input attribute. We proposed a GA-FIS (genetic algorithm based fuzzy inference systems) model that is capable of performing pattern classification, rule extraction and optimization all in one stage. First objective is to automatically develop best combination of fuzzy rules from raw data in the real world without human experts. This paper is on enforcing the interpretability of the classification system standing of the rules extracted from the numerical measured data.
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