Perneel, C.
Themlin, JM.
Renders, JM.
Acheroy, Marc
In this paper, the fuzzy logic theory is used to build a specific decision-making system for heuristic search algorithms. Such algorithms are typically used for expert systems. To improve the performance of the overall system, a set of important parameters of the decision-making system is identified. Two optimization methods for the learning of the optimum parameters, namely genetic algorithms and gradient-descent techniques based on a neural network formulation of the problem, are used to obtain an improvement of the performance. The decision-making system and both optimization methods are tested on a target recognition system.
Bibliographic reference |
Perneel, C. ; Themlin, JM. ; Renders, JM. ; Acheroy, Marc. Optimization of Fuzzy Expert-systems Using Genetic Algorithms and Neural Networks. In: IEEE Transactions on Fuzzy Systems, Vol. 3, no. 3, p. 300-312 (1995) |
Permanent URL |
http://hdl.handle.net/2078.1/47858 |