Self-learning fuzzy controllers based on temporal backpropagation
- 1 September 1992
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 3 (5) , 714-723
- https://doi.org/10.1109/72.159060
Abstract
A generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner is presented. This methodology, termed temporal backpropagation, is model-sensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules of human experts or automatically derive the fuzzy if-then rules if human experts are not available. The inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.<>Keywords
This publication has 6 references indexed in Scilit:
- Refinement of Approximate Reasoning-based Controllers by Reinforcement LearningPublished by Elsevier ,1991
- A self-learning rule-based controller employing approximate reasoning and neural net conceptsInternational Journal of Intelligent Systems, 1991
- Neural networks for self-learning control systemsIEEE Control Systems Magazine, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989
- Neuronlike adaptive elements that can solve difficult learning control problemsIEEE Transactions on Systems, Man, and Cybernetics, 1983
- Polynomial Theory of Complex SystemsIEEE Transactions on Systems, Man, and Cybernetics, 1971