Abstract
We describe a fuzzy control based on a neural network, which is obtained by merging the advantages of a neural network, a competitive algorithm, and fuzzy control. This adaptive fuzzy control system can deal with data sampled by a neural network. From such training data, it can produce more reasonable fuzzy rules by a competitive (clustering) algorithm, and finally control the object by the optimized fuzzy rules. This is not a simple combination of the three methods, but a merger into one control system. Some experiments and future considerations are also given.
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This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003
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Sugisaka, M., Dai, F. & Kimura, H. Application of a neuro-fuzzy system to control a mobile vehicle. Artif Life Robotics 8, 77–82 (2004). https://doi.org/10.1007/s10015-004-0292-x
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DOI: https://doi.org/10.1007/s10015-004-0292-x