基于Agent和数据切片的分布式神经网络协同学习研究  

Neural Networks' Distributed Cooperative Learning Strategy Based on Agent and Chips

作　　者：杨博[1] 王亚东[1] 苏小红[1] 唐降龙[1] 

机构地区：[1]哈尔滨工业大学计算机学院,哈尔滨150001

出　　处：《计算机研究与发展》2006年第12期2096-2103,共8页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(30370798;60273083)~~

摘　　要：针对目前神经网络在处理类似生物信息数据库这类较大规模数据时,遇到的大规模数据处理耗时过长、内存资源不足等问题·在分析当前神经网络分布式学习的基础上,提出了一种新的基于Agent和切片思想的分布式神经网络协同训练算法·通过对训练样本和训练过程的有效切分,整个样本集的学习被分配到一个分布式神经网络集群环境中进行协同训练,同时通过竞争筛选机制,使得学习性能较好的训练个体能有效地在神经网络群中迁移,以获得较多的资源进行学习·理论分析论证了该方法不仅能有效提高神经网络向目标解收敛的成功率,同时也具有较高的并行计算性能,以加快向目标解逼近的速度·最后,该方法被应用到了蛋白质二级结构预测这一生物信息学领域的问题上·结果显示,该分布式学习算法不仅能有效地处理大规模样本集的学习,同时也改进了训练得到的神经网络性能·To solve the bottleneck of memory and running time problem in protein structure predicting with large-scale data set, a neural networks＇ distributed learning algorithm is studied. Based on the analysis of the previous methods of distributed learning algorithm in a distributed computing environment with a large number of processors, a new distributed neural network based on multi-agents, and a learning algorithm based on chips to work on the distributed neural networks are created, which approach the global optimum by competition from a group of agents that have the different training sample chips to process. It is proved that the new distributed learning algorithm is better on balancing the parallel computing efficiency and the success rate of learning compared with the previous algorithms. The experiment results demonstrate that this method is feasible and effective in practical applications when used in protein secondary structure predicting with large size of amino-acid sequence.

关 键 词：多层人工神经网络 分布式学习算法 生物信息学 蛋白质结构预测 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]