Abstract
A non-fuzzy rule-based system for process control and general applications is described. The system uses a similar rule-base as fuzzy systems, but it does not use fuzzy variables. The items of the knowledge-base are only real numbers and the evaluation mechanism is based on a n-dimensional interpolation. It is very simple and computationally fast. Next a genetic algorithm-based optimisation of the rule-base is shown. The proposed approach is demonstrated on process control simulations and real-time examples as well.
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Sekaj, I. (2000). Non-Fuzzy Knowledge-Rule-Based Controllers and their Optimisation by Means of Genetic Algorithms. In: Hampel, R., Wagenknecht, M., Chaker, N. (eds) Fuzzy Control. Advances in Soft Computing, vol 6. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1841-3_17
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DOI: https://doi.org/10.1007/978-3-7908-1841-3_17
Publisher Name: Physica, Heidelberg
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Online ISBN: 978-3-7908-1841-3
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