Abstract
This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods.
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Pratama, M. et al. (2011). Genetic Dynamic Fuzzy Neural Network (GDFNN) for Nonlinear System Identification. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_61
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DOI: https://doi.org/10.1007/978-3-642-21090-7_61
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