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前馈神经网络的模型选择及其混合模型的研究
其他题名Study on Model Selection of Feedforward Neural Networks and Their Hybrid Models
邢红杰
学位类型工学博士
导师胡包钢
2007-06-02
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词前馈神经网络 模型选择 自适应模糊c均值聚类 混合专家模型 Feedforward Neural Network Model Selection Adaptive Fuzzy C-means Clustering Mixture Of Experts
摘要前馈神经网络在实际当中得到了广泛的应用,但是其模型选择仍是神经网络研究领域亟待解决的问题,至今仍无严格的理论指导和统一的选取准则。同时,随着新理论和新算法的不断出现,单个前馈神经网络在分类(或函数逼近)问题上越来越难以取得更高的性能。因此,为了获得更高的分类准确率(或逼近精度),对不同类型的前馈神经网络进行混合不失为一种切实可行的办法。 本文从模型选择和混合策略两个角度对前馈神经网络进行了一些研究,主要工作包括以下四个方面: 1. 基于信息论中的互信息,为多层感知器提出了一种两阶段构造方法。这种构造方法能够自动剔除不相关的输入节点和冗余的隐含节点。通过实验验证了它的有效性,且与Engelbrecht所提出的基于敏感度分析的结构选取方法对比表明,这种方法在基准数据集上能够取得更强的泛化能力。另外,研究表明:对于不同区间或同一区间 上分布不同的样本,基于互信息的方法能够提供比基于敏感度分析的方法更为准确的相关度;当输入特征之间函数相关时,基于互信息的方法则比基于敏感度分析的方法更为有效; 2. 提出了一种训练椭球基函数神经网络的混合学习算法。实验结果表明此算法能够提高传统的径向基函数神经网络的分类能力。与使用相同学习策略的径向基函数神经网络相比,在达到相同(或者更高)的测试准确率要求下,椭球基函数神经网络需要更少的训练次数和训练时间; 3. 从没有类别个数信息的无类标数据分类问题出发,提出了一个基于自适应模糊c均值的混合专家模型,并通过实验验证了其有效性。此外,将模型推广到了半监督分类情况并给出了相应的模型构造方法,与Fung和Mangasarian所提出的CVS3<上标!>VM模型相比,此拓展方法可以产生更佳的分类性能; 4. 提出了高斯{契比雪夫神经网络模型。详细阐述了训练该模型的误差反传算法,并通过实验表明了它能够改善高斯神经网络的泛化能力、逼近能力和抗噪声能力。在相同的拓扑结构和参数设置下,高斯-契比雪夫神经网络能比Gauss-Sigmoid神经网络更快更准确地逼近相同的函数。
其他摘要In the real world, feedforward neural networks have been applied extensively. However, its model selection is still an open problem in the field of neural networks because there is no a strict theoretical guidance or a standard selection criteria. Moreover, with the continuous emergence of new theories and new algorithms, it is more and more diffcult for a single feedforward neural network, which is used for classification (or function approximation), to achieve better performance. Thus, to produce higher classification accuracy rate (or approximation accuracy), mixing the feedforward neural networks in different types together may be a feasible solution. In this dissertation, feedforward neural networks are studied from the perspectives of model selection and hybrid strategy. The main contributions of this dissertation contain five aspects below. 1) Basing on mutual information in information theory, we have proposed a two-phase construction approach for multilayer perceptrons. The approach can remove the irrelevant input units and redundant hidden units automatically. The experiment results show the efficiency of our method. Furthermore, compared with its related work, the proposed strategy exhibits better generalization ability in dealing with the benchmark data sets. 2) We have proposed a hybrid learning method for elliptical basis function neural networks. The experiment results show that it can improve the classification abilities of the traditional radial basis function neural networks. To achieve the same (or better) test accuracy rate, the elliptical basis function neural network trained with the proposed method needs far fewer training epoches and training time in comparison with the radial basis function neural network trained in the same manner. 3) For the unlabeled data classification with no information of class labels, we have proposed a model named “an adaptive fuzzy c-means clustering based mixtures of experts”. Experiment results show its better performance. The extension version of the proposed model for semi-supervised classification has been presented. The experiment result also show that the extension version of the proposed method can achieve better performance in comparison with its related work. 4) The Gauss-Chebyshev neural network has been proposed, and the training method of it, i.e. back propagation algorithm, has been presented in detail. Experiment results show that Gauss-Chebyshev neural network can improve the generalization, approximation, and anti-noise abilities of Gaussian neural network. With the same topological structure and the same settings of parameters, Gauss-Chebyshev neural network can approximate a function more quickly and exactly in comparison with Gauss-Sigmoid neural network.
馆藏号XWLW1069
其他标识符200318014603033
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5995
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
邢红杰. 前馈神经网络的模型选择及其混合模型的研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2007.
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