An introduction to intelligent and autonomous control
Learning control: methods, needs and architectures
An introduction to intelligent and autonomous control
A course in fuzzy systems and control
A course in fuzzy systems and control
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A POD-Based Center Selection for RBF Neural Network in Time Series Prediction Problems
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
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Conventional hill-climbing type adaptive algorithms suffer from large transient error in abrupt changing environments. In this paper, we suggest a novel idea to address this problem. A data re-initialization (DR) scheme is proposed using multiple models approach with special initialization technique to smooth transition between different environments. The design is based on adaptive fuzzy nearest neighbor clustering algorithm with on-line enhancements. The fast adaptation is realized by the data re-initialization and switching between different models; learning is realized by recording and retrieving the trained up models. The algorithm is conceptually simple and feasible for real-time applications. The performance of the algorithm is tested with simulation studies.