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IEA/AIE '89 Proceedings of the 2nd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Adaptive fuzzy systems and control: design and stability analysis
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Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
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Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
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Fuzzy Sets and Systems
Connectionist Structures of Type 2 Fuzzy Inference Systems
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
Introduction to Evolutionary Computing
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ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Rough neuro-fuzzy structures for classification with missing data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modular type-2 neuro-fuzzy systems
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Neuro-fuzzy systems with relation matrix
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
On classification with missing data using rough-neuro-fuzzy systems
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
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ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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The acquisition of the knowledge which is useful for developing of artificial intelligence systems is still a problem. We usually ask experts, apply historical data or reap the results of mensuration from a real simulation of the object. In the paper we propose a new algorithm to generate a representative training set. The algorithm is based on analytical or discrete model of the object with applied the k---nn and genetic algorithms. In this paper it is presented the control case of the issue illustrated by well known truck backer---upper problem. The obtained training set can be used for training many AI systems such as neural networks, fuzzy and neuro---fuzzy architectures and k---nn systems.