GA-RBFGenetic Algorithm - Radial-Basis Function
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Tables 1 and 2 illustrate that, from the training success rate (the success times within 50 training times) aspect, GA optimized RBF algorithm is superior to the traditional RBF algorithm; from the training error and test error aspect, RBF and GA-RBF-L algorithm are equivalent, or slightly better than GA-RBF algorithm; from the operation time aspect, the operation time of GA optimized RBF algorithm is slightly longer, because running the genetic algorithm will take longer time; from the recognition precision aspect, the GA-RBF-L algorithm's classification precision is the best.
The network structure will affect the generalization capability of the algorithm, comparing RBF, GA-RBF, and GARBF-L; while the RBF algorithm gets the small training error, its recognition precision is not as good as GA-RBFL algorithm whose hidden layer neurons are fewer.
Traditional Neural networks algorithm RBF GA-RBF GA-RBF-L Training success rate, % 86 100 100 Training error 0.22 0.36 0.29 Test error 1.78 1.97 1.61 Number of hidden neurons 44 28 28 Operation time, s 1.21 1.62 1.62 + 0.22 Classification accuracy, % 89 87 97 Table 2: The comparison of the performance of each algorithm for wine data set.