【作者】 魏崴；

【导师】 秦娟英； 王攀；

【作者基本信息】 武汉理工大学 ， 控制理论与控制工程， 2004， 硕士

【摘要】 模块化神经网络是一种由多个智能体组成的学习机模型，是由多个神经网络以协作或竞争的方式构建的学习系统。其基本思想是以多个神经网络来探索各个子学习机的不同行为，以期提高整个学习系统的学习能力、系统精度和泛化性能。大量的实例研究表明，模块化神经网络在泛化能力和可靠性上比单一神经网络都有所提高，为我们提供了一条问题求解的新途径。而且最近所提出的各种理论解释也都证实了一些常用模块化方法的有效性。 由于认识到模块化神经网络所蕴含的巨大潜力和应用前景，大量研究者涌入该领域，理论和实践成果不断涌现，使得模块化神经网络成为目前许多领域的一个相当活跃的研究热点，譬如模式识别、神经计算、机器学习、信息处理等。 当前，模块化神经网络的研究主要集中在两个方面，即如何将多个神经网络的输出结论进行结合以及如何生成系统中的个体网络。本文的工作主要集中在结论合成方面，提出子网络选择性集成的方案，提出一种新的层次模块化神经网络模型。同时本文还对模块化神经网络的鲁棒性能进行了研究。 全文共分五章。 第一章，本文首先从需求，神经生理学和社会科学等角度阐述了模块化神经网络出现的可能和必然，然后从理论与应用两个方面介绍了模块化神经网络的研究现状及应用前景。第二章，本文提出了一种模块化神经网络的鲁棒学习算法，实验研究表明模块化神经网络的鲁棒学习算法对于污染样本的学习能获得较好的鲁棒性能和较高的学习精度，特别是在模型较复杂时，该算法的效果尤为明显。本文在第三章讨论了模块化神经网络自适应集成的一些问题，如集成权的动态生成，子神经网络配置的最优化，以及子神经网络的重组等。第四章，本文应用分而治之的思想提出了一种层次模块化神经网络新方法—三层结构的模块化神经网络模型，在提高算法性能方面有一定优势。武汉理工大学硕十学位论文对比实验研究也表明该算法有效地提高了系统的泛化能力和算法稳定性。最后一章，即第5章，对全文工作扼要总结，并就日后需进一步开展的工作进行了展望。更多还原

【Abstract】 Modular neural network is a new learning model based on multi-agents, whose decisions are combined in a fashion of competition and cooperation of a number of artificial neural networks. The essential idea of the learning system is to improve the performance of the overall system by searching the difference of the sub-Neural networks. A great deal of successful applications demonstrates that modular neural network outperforms single neural network in terms of generalization and reliability and undoubtedly provides for us with a new tool for problem-solving. Moreover, various theoretical explanations proposed recently justify the effectiveness of some conventional methods for modular neural network.Having recognized that modular neural network has an enormous potential and bright prospect in application, a large number of researchers plunge themselves into the field, yielding a lot of relevant theories and application achievements. At present modular neural networks is a rathers hot topic in many diverse areas such as pattern recognition, neuro-computation, machine learning, information processing.The research for modular neural network concentrates on two aspects: how to combine the decisions of the component networks, and how to generate the component networks in the entire system. In this paper, the first aspect is mainly studied, a scheme of selective ensemble ispresented, and a new architecture of modular neural networks is presented. At the same time, the robust algorithm of modular neural networks is studied.This paper composes five chapters in all. Chapter 1 expatiates the probability and necessity of the generation of modular aeural networks from the viewpoints of commands, neurobiology and social science. In Chapter 2 a robust learning algorithm of modular neural networks based on the theory of robust regression is presented. Empirical study demonstrates that the robust learning algorithm of MNN has better precision and generalization than both modular neural networks and single neural network with the same robust algorithm, when trained under the contained dada. In Chapter 3, problems of adaptive combination are discussed. In Chapter 4 a new architecture of multiple neural networks based on modularity is presented, which is named hierarchical modular neural networks. Finally in Chapter 5, contributions of this paper are summarized and several issues for future work are indicated.更多还原