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文件名称:神经网络结构优化算法
文件大小:677KB
文件格式:PDF
更新时间:2021-06-13 04:43:13
AMGA算法
Abstract— Training neural networks is a complex task
of great importance in the supervised learning field of
research. We intend to show the superiority (time
performance and quality of solution) of the new
metaheuristic bat algorithm (BA) over other more
―standard‖ algorithms in neural network training. In this
work we tackle this problem with five algorithms, and
try to over a set of results that could hopefully foster
future comparisons by using a standard dataset
(Proben1: selected benchmark composed of problems
arising in the field of Medicine) and presentation of the
results. We have selected two gradient descent
algorithms: Back propagation and Levenberg-
Marquardt, and three population based heuristic: Bat
Algorithm, Genetic Algorithm, and Particle Swarm
Optimization. Our conclusions clearly establish the
advantages of the new metaheuristic bat algorithm over
the other algorithms in the context of eLearning