A hybrid Gravitational Search Algorithm–Genetic Algorithm for neural network training
Publish place: 21th Iranian Conference on Electric Engineering
Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICEE21_848
تاریخ نمایه سازی: 27 مرداد 1392
Abstract:
Tuning optimum parameter of neural networks, such as weights and biases, has major effects on their performance improvement. Estimation of optimum values for these parameters requires strong and effective training methods, so that the error of the training data reaches its minimum. This paper presents, a suitable training method for optimizing neural networks parameters using a novel hybrid GA-GSA algorithm. Extensive experimental results on different benchmarks show that the hybrid algorithm, performs equal to or better than standard GSA, and backpropagation algorithm
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Authors
Saeide Sheikhpour
Faculty of Electrical Engineering, University of Birjand, Iran