Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Large-scale systems: modeling, control, and fuzzy logic
Large-scale systems: modeling, control, and fuzzy logic
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Fuzzy Control
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
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The application of fuzzy control to large-scale complex systems is not a trivial task. For such systems the number of the fuzzy IF-THEN rules exponentially explodes. If we have l possible linguistic properties for each of n variables, with which we will have ln possible combinations of input values. Large-scale systems require special approaches for modeling and control. In our work the sensory fusion method is studied in an attempt to reduce the size of the inference engine for large-scale systems. This method reduces the number of rules considerably. But, in order to do so, the adequate parameters should be estimated, which, in the traditional way, depends on the experience and knowledge of a skilled operator. In this work, we are proposing a method to automatically estimate the corresponding parameters for the sensory fusion rule base reduction method to be applied to fuzzy control complex systems. In our approach, the parameters of the sensory fusion method are found through the use of genetic algorithms. The implementation process, the simulation experiments, as well as some results are described in the paper.