文档介绍：山东师范大学博士学位论文真实验表明,该算法能有效的提高神经树网络模型的进化收敛速度和误差精度。 3、结合集成学****提出了神经树网络集成的概念,对神经树网络集成在数据挖掘领域中的分类和预测问题上的应用进行了研究并提出了若干相关模型: (1) 在分类问题方面:提出了一种以神经树网络模型为基本分类器的神经树网络集成方法,以“输出结果处理法”为主要研究对象,构建了基于纠错码的神经树网络集成分类模型,同时给出了该集成分类模型的算法设计和工作流程,最后在若干UCI数据集上对该集成分类模型的有效性和优越性进行了实验验证; 另外,提出了基于Bagging和Boosting的神经树网络集成分类模型,同时给出了两种集成分类模型的算法设计及其在若干UCI数据集上的仿真实验对比。(2) 在预测问题方面:提出了基于Bagging和Boosting的神经树网络集成预测模型,并以非线性函数模拟为应用对象,与相关模型的性能分别进行了仿真实验对比。 4、根据神经树网络模型研究的实验需求,结合面向对象技术和软件Matlab R2008a,在Visual Studio .NET 2008开发环境中以C#语言为基础,构建了神经树网络模型仿真实验平台。该平台具有实验数据预处理、神经树网络模型构建、神经树网络模型优化算法集成以及实验结果图形化展现等功能。最后,该仿真实验平台被用于解决房地产价格指数预测和铁路客运量预测两个实际问题。英文摘要:An artificial work (ANN) is putational model that is inspired by the structure and functional aspects of biological neural networks. A work consists of an interconnected group of artificial neurons, and aims to realize some special function by simulatingsome mechanism of the brain. In most cases, an ANN is an adaptive system that changes its structure based on external or internal information that flows through work during the learning phase. ANN with its massively parallel processing, fault tolerance, anization and adaptive ability, has e a powerful tool to plex problems. At present, there exist many NN models, however multilayer feedforward work (MFNN) is one of the most popular models which now being studied and applied. It has conspicuous hierarchical structure formed by simple neural units, good nonlinear quality, flexible and effective learning ways and robust simulation capabilities for nonlinear system, so it has been widely used in system identification, datamining, signal processing and 山东师范大学博士学位论文 fault diagnosis, etc. Although Hornik, etc had proved that MFNN with only a single hidden layer could approximate any complex functions, the method how to find the reasonable structure and the corresponding parameter values for ANN is still a NP-hard problem. Therefore, MFN