Enhancing neural control systems by fuzzy logic and evolutionary reinforcement

DOI10.1007/BF01414164zbMath0925.93531OpenAlexW2129126297MaRDI QIDQ1271680

K. Appert

Publication date: 8 June 1999

Published in: Neural Computing and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01414164

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Fuzzy control/observation systems (93C42) Neural networks for/in biological studies, artificial life and related topics (92B20)

Cites Work

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• Fuzzy input-output controllers are universal approximators
• Fuzzy logic in control systems: fuzzy logic controller. I
• A Nonlinear Adaptive Controller: A Comparison between Fuzzy Logic Control and Neurocontrol
• Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications
• Genetic algorithms for fuzzy control.1. Offline system development and application
• Fuzzy sets
• Genetic Algorithms and the Optimal Allocation of Trials

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