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Simplification of fuzzy-neural systems using similarity analysis
Summary
This study introduces a fuzzy neural network system (FNNS) that simplifies fuzzy inference systems by reducing redundant rules and combining similar nodes. The FNNS maintains desirable performance with fewer parameters and rules.
Area of Science:
- Artificial Intelligence
- Computational Intelligence
- Machine Learning
Background:
- Fuzzy inference systems (FIS) are widely used but can suffer from complexity due to numerous fuzzy rules and linguistic terms.
- Existing fuzzy neural network systems (FNNS) may not efficiently reduce this complexity, impacting performance and resource utilization.
Purpose of the Study:
- To propose a novel fuzzy neural network system (FNNS) designed to reduce the complexity of fuzzy inference systems.
- To introduce methods for eliminating redundant fuzzy rules and combining similar fuzzy sets within the FNNS architecture.
- To develop an efficient on-line initialization method for FNNS parameters.
Main Methods:
- A fuzzy similarity measure for fuzzy rules was developed to identify and eliminate redundant logical rules.
- A fuzzy similarity measure for fuzzy sets was applied to merge similar input linguistic term nodes, simplifying the network structure.
- An efficient on-line initialization technique was designed for setting the initial parameters of the FNNS.
Main Results:
- The proposed FNNS effectively reduces the number of fuzzy logical rules in the fuzzy inference system.
- The system successfully combines similar input linguistic term nodes, leading to a reduction in fuzzy neural network complexity.
- Computer simulations demonstrated that the FNNS achieves desirable performance even with a reduced number of rules and adjustable parameters.
Conclusions:
- The developed FNNS offers an effective approach to simplifying fuzzy inference systems while maintaining performance.
- The methods for rule and fuzzy set similarity measurement contribute to reducing computational complexity and improving efficiency.
- The FNNS presents a viable solution for implementing complex fuzzy logic with enhanced performance and reduced resource requirements.
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