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作 者:张长胜[1] 欧阳丹彤[2] 岳娜[1] 张永刚[1]
机构地区:[1]吉林大学计算机科学与技术学院,长春130012 [2]吉林大学符号计算与知识工程教育部重点实验室,长春130012
出 处:《吉林大学学报(理学版)》2008年第4期675-680,共6页Journal of Jilin University:Science Edition
基 金:国家自然科学基金(批准号:60773097);教育部新世纪优秀人才支持计划项目基金(批准号:60273080)
摘 要:针对遗传算法与神经网络结合方式中存在的早熟收敛、泛化能力弱等问题,提出一种交替使用遗传算法和Levenberg-Marquardt算法优化神经网络的混合学习算法(GALM算法).该算法先通过遗传算法粗调得到一组全局最优近似解,再以该近似解为初值,交替使用遗传算法和LM算法优化神经网络训练,直至发现满意的网络参数.实验结果表明,新算法提高了网络的学习能力和收敛速度.In order to overcome the insufficiencies of premature convergence and weak extensive ability in the combination of Genetic Algorithm and Artificial Neural Networks, we proposed a new hybrid study algorithm-GALM, which uses the Genetic Algorithm and Levenberg-Marquardt in turn to optimize the neural network. This algorithm mainly includes two stages: First a group of solutions were obtained which approxi- mate the global optimum through cursorily adjusting the genetic algorithm. Then these approximate solutions were taken as the initial values, the GA and LM algorithms were used to optimize the neural network training in turn until the satisfactory network parameters were found. Finally we compared the GALM algorithm with other relevant algorithms through experimentation. The results indicate that our algorithm can effectively overcome the problem about falling into the local optimal solutions, and remarkably improves the network learning capability and the convergence rate.
分 类 号:TP18[自动化与计算机技术—控制理论与控制工程]
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