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Hybrid System Geno-Fuzzified Neural Network For Solving Some Classification Problems | ||
| Journal of Basrah Researches (Sciences) | ||
| Article 1, Volume 37, Issue 2, 2011, Pages 104-117 PDF (0 K) | ||
| Authors | ||
| Shatha Faleh Hendy; Raidah Salim Khaudeyer | ||
| Abstract | ||
| This paper presented a hybrid method consisting of three intelligent systems (artificial neural networks, fuzzy logic, genetic algorithms), since these systems are effective in solving different issues but all the system suffers from some problems that reduce efficiency, so it was to integrate these systems with some To give the system benefit from the advantages of each method and encroaches on the disadvantages. We used in this research method of a hybrid resulting from a combination of fuzzy logic and neural networks, as used fuzzy logic to fuzzified training data and weights used in the neural network, and this method is called fuzzified neural networks, which gives the network a greater ability to generalize and accelerate the convergence process, but this method suffers of a problem in determining the number of fuzzy sets and optimal fuzzy weights, as the experiment method used to select it. To resolve this problem, genetic algorithm was used to determine the best number of fuzzy sets and the best fuzzy weights through research, which makes the network more efficiently trained. | ||
| Keywords | ||
| fuzzy system; neural networks; genetic algorithm; fuzzified neural networks | ||
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