New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing
New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing
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用于系统建模、模式分类和图像处理的新软计算技术
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发表时间：
2004
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通讯作者：
L. Rutkowski
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作者：
L. Rutkowski
1 引言 - I 非平稳环境中的概率神经网络 - 2 用于构建概率神经网络的核函数 - 2.1 引言 - 2.2 帕尔森核的应用 - 2.3 正交级数的应用 - 2.4 结论 - 3 概率神经网络简介 - 3.1 引言 - 3.2 用于密度估计的概率神经网络 - 3.3 平稳环境中的广义回归神经网络 - 3.4 平稳环境中用于模式分类的概率神经网络 - 3.5 结论 - 4 时变环境中的一般学习过程 - 4.1 引言 - 4.2 问题描述 - 4.3 一般学习过程的介绍 - 4.4 一般学习过程的收敛性 - 4.4.1 局部性质 - 4.4.2 全局性质 - 4.4.3 收敛速度 - 4.5 准平稳环境 - 4.6 预测问题 - 4.7 结论 - 5 时变环境中的广义回归神经网络 - 5.1 引言 - 5.2 问题描述及广义回归神经网络的介绍 - 5.3 时变环境中广义回归神经网络的收敛性 - 5.3.1 基于帕尔森核的广义回归神经网络 - 5.3.2 基于正交级数的广义回归神经网络 - 5.4 收敛速度 - 5.5 具有乘性非平稳性的系统建模 - 5.6 具有加性非平稳性的系统建模 - 5.7 具有“尺度变化”和“可移动参数”类型非平稳性的系统建模 - 5.8 具有递减非平稳性的系统建模 - 5.9 结论 - 6 时变环境中用于模式分类的概率神经网络 - 6.1 引言 - 6.2 问题描述及分类规则的介绍 - 6.3 分类规则的渐近最优性 - 6.4 分类规则的收敛速度 - 6.5 基于帕尔森核的分类过程 - 6.6 基于正交级数的分类过程 - 6.7 “可移动参数”类型的非平稳性 - 6.8 准平稳环境中的分类 - 6.9 模拟结果 - 6.9.1 用于估计时变概率密度的概率神经网络 - 6.9.2 用于时变环境中分类的概率神经网络 - 6.10 结论 - II 用于图像压缩的软计算技术 - 7 用于图像压缩的矢量量化 - 7.1 引言 - 7.2 预处理 - 7.3 问题描述 - 7.4 基于神经网络的矢量量化算法 - 7.5 结论 - 8 差分脉冲编码调制技术 - 8.1 引言 - 8.2 标量情况 - 8.3 矢量情况 - 8.4 神经网络的应用 - 8.5 结论 - 9 预测矢量量化方案 - 9.1 引言 - 9.2 预测矢量量化方案的描述 - 9.3 结论 - 10 预测器的设计 - 10.1 引言 - 10.2 最优矢量线性预测器 - 10.3 从经验数据设计线性预测器 - 10.4 基于神经网络的预测器 - 10.5 结论 - 11 码本的设计 - 11.1 引言 - 11.2 竞争算法 - 11.3 预处理 - 11.4 初始码本的选择 - 11.5 结论 - 12 预测矢量量化方案的设计 - 12.1 引言 - 12.2 开环设计 - 12.3 闭环设计 - 12.4 改进的闭环设计 - 12.5 神经预测矢量量化设计 - 12.6 结论 - III 用于神经网络学习的递归最小二乘法及其脉动实现 - 13 递归最小二乘学习算法族 - 13.1 引言 - 13.2 符号 - 13.3 问题描述 - 13.4 递归最小二乘学习算法 - 13.4.1 单层神经网络 - 13.4.2 多层神经网络 - 13.5 QQ - 递归最小二乘学习算法 - 13.5.1 单层 - 13.5.2 多层神经网络 - 13.6 UD - 递归最小二乘学习算法 - 13.6.1 单层 - 13.6.2 多层神经网络 - 13.7 模拟结果 - 13.7.1 性能评估 - 13.8 结论 - 14 递归最小二乘学习算法的脉动实现 - 14.1 引言 - 14.2 回忆阶段的脉动架构 - 14.3 用于ETB递归最小二乘学习算法的脉动架构 - 14.4 用于递归最小二乘学习算法的脉动架构 - 14.5 脉动架构的性能评估 - 14.5.1 回忆阶段 - 14.5.2 学习阶段：ETB递归最小二乘算法 - 14.5.3 学习阶段：递归最小二乘算法 - 14.6 结论 - 参考文献
1 Introduction.- I Probabilistic Neural Networks in a Non-stationary Environment.- 2 Kernel Functions for Construction of Probabilistic Neural Networks.- 2.1 Introduction.- 2.2 Application of the Parzen kernel.- 2.3 Application of the orthogonal series.- 2.4 Concluding remarks.- 3 Introduction to Probabilistic Neural Networks.- 3.1 Introduction.- 3.2 Probabilistic neural networks for density estimation.- 3.3 General regression neural networks in a stationary environment.- 3.4 Probabilistic neural networks for pattern classification in a stationary environment.- 3.5 Concluding remarks.- 4 General Learning Procedure in a Time-Varying Environment.- 4.1 Introduction.- 4.2 Problem description.- 4.3 Presentation of the general learning procedure.- 4.4 Convergence of general learning procedure.- 4.4.1 Local properties.- 4.4.2 Global properties.- 4.4.3 Speed of convergence.- 4.5 Quasi-stationary environment.- 4.6 Problem of prediction.- 4.7 Concluding remarks.- 5 Generalized Regression Neural Networks in a Time-Varying Environment.- 5.1 Introduction.- 5.2 Problem description and presentation of the GRNN.- 5.3 Convergence of the GRNN in a time-varying environment.- 5.3.1 The GRNN based on Parzen kernels.- 5.3.2 The GRNN based on the orthogonal series.- 5.4 Speed of convergence.- 5.5 Modelling of systems with multiplicative non-stationarity.- 5.6 Modelling of systems with additive non-stationarity.- 5.7 Modelling of systems with non-stationarity of the "scale change" and "movable argument" type.- 5.8 Modelling of systems with a diminishing non-stationarity.- 5.9 Concluding remarks.- 6 Probabilistic Neural Networks for Pattern Classification in a Time-Varying Environment.- 6.1 Introduction.- 6.2 Problem description and presentation of classification rules.- 6.3 Asymptotic optymality of classification rules.- 6.4 Speed of convergence of classification rules.- 6.5 Classification procedures based on the Parzen kernels.- 6.6 Classification procedures based on the orthogonal series.- 6.7 Non-stationarity of the "movable argument" type.- 6.8 Classification in the case of a quasi-stationary environment.- 6.9 Simulation results.- 6.9.1 PNN for estimation of a time-varying probability density.- 6.9.2 PNN for classification in a time-varying environment.- 6.10 Concluding remarks.- II Soft Computing Techniques for Image Compression.- 7 Vector Quantization for Image Compression.- 7.1 Introduction.- 7.2 Preprocessing.- 7.3 Problem description.- 7.4 VQ algorithm based on neural network.- 7.5 Concluding remarks.- 8 The DPCM Technique.- 8.1 Introduction.- 8.2 Scalar case.- 8.3 Vector case.- 8.4 Application of neural network.- 8.5 Concluding remarks.- 9 The PVQ Scheme.- 9.1 Introduction.- 9.2 Description of the PVQ scheme.- 9.3 Concluding remarks.- 10 Design of the Predictor.- 10.1 Introduction.- 10.2 Optimal vector linear predictor.- 10.3 Linear predictor design from empirical data.- 10.4 Predictor based on neural networks.- 10.5 Concluding remarks.- 11 Design of the Code-book.- 11.1 Introduction.- 11.2 Competitive algorithms.- 11.3 Preprocessing.- 11.4 Selection of initial code-book.- 11.5 Concluding remarks.- 12 Design of the PVQ Schemes.- 12.1 Introduction.- 12.2 Open-loop design.- 12.3 Closed-loop design.- 12.4 Modified closed-loop design.- 12.5 Neural PVQ design.- 12.6 Concluding remarks.- III Recursive Least Squares Methods for Neural Network Learning and their Systolic Implementations.- 13 A Family of the RLS Learning Algorithms.- 13.1 Introduction.- 13.2 Notation.- 13.3 Problem description.- 13.4 RLS learning algorithms.- 13.4.1 Single layer neural network.- 13.4.2 Multi-layer neural networks.- 13.5 QQ-RLS learning algorithms.- 13.5.1 Single layer.- 13.5.2 Multi-layer neural network.- 13.6 UD-RLS learning algorithms.- 13.6.1 Single layer.- 13.6.2 Multi-layer neural networks.- 13.7 Simulation results.- 13.7.1 Performance evaluation.- 13.8 Concluding remarks.- 14 Systolic Implementations of the RLS Learning Algorithms.- 14.1 Introduction.- 14.2 Systolic architecture for the recall phase.- 14.3 Systolic architectures for the ETB RLS learning algorithms.- 14.4 Systolic architectures for the RLS learning algorithms.- 14.5 Performance evaluation of systolic architectures.- 14.5.1 The recall phase.- 14.5.2 The learning phase: the ETB RLS algorithm.- 14.5.3 The learning phase: the RLS algorithm.- 14.6 Concluding remarks.- References.