Design and Verification of an Interval Type-2 Fuzzy Neural Network Based on Improved Particle Swarm Optimization
Source abstract
Abstract
In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fuzzy Logic and Control Systems · Neural Networks and Applications · Machine Learning and ELM
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan
School of Intelligence, National Taichung University of Science and Technology, Taichung 404, Taiwan
Funding
the Ministry of Science and Technology of the Republic of China
References (29)
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