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Jingdong book

复杂系统支持向量机建模与故障预报

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Jingdong book

复杂系统支持向量机建模与故障预报

Name: 复杂系统支持向量机建模与故障预报
Brand: Jingdong book
SKU: 3118010221
Price: 45.42 USD
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《复杂系统支持向量机建模与故障预报》在介绍国内外故障预报方法的基础上，重点阐述了基于支持向量机的故障预报方法；深入研究了基于无标定样本的单类支持向量机故障预报模型；提出了一种自适应动态无偏最小二乘支持向量机故障预报模型；针对多输入多输出样本，提出了基于ε不敏感损失函数的多输出支持向量回归机模型和多输出最小二乘支持向量机模型；为了衡量系统的异常程度，提出了基于支持向量机概率密度估计模型的系统异常程度衡量方法；最后探讨了复杂系统故障预报方面的最新研究进展。

Catalogue

第1章绪论
1.1 引言
1.2 故障预报方法的研究现状
1.2.1 模型驱动方法
1.2.2 数据驱动方法
1.2.3 定性知识驱动方法
1.3 基于支持向量机的故障预报方法的研究现状
1.3.1 故障预报问题的特点
1.3.2 统计学习理论
1.3.3 支持向量机
1.3.4 研究现状
1.4 本书的内容

第2章基于支持向量预选取的one-class SVM故障预报模型
2.1 引言
2.2 问题描述
2.3 one-class SVM模型
2.3.1 超平面
2.3.2 超球体
2.4 支持向量预选取
2.4.1 超平面的支持向量预选取
2.4.2 超球体的支持向量预选取
2.4.3 确定选取样本的个数
2.4.4 算法复杂度分析
2.5 故障预报算法步骤
2.6 仿真实例和结果分析
2.6.1 同心圆环样本支持向量预选取
2.6.2 电机数据故障预报
2.7 本章小结

第3章自适应动态无偏LSSVM故障预报模型
3.1 引言
3.2 最小二乘支持向量机及在线算法
3.3 动态无偏最小二乘支持向量机
3.3.1 初始预测模型
3.3.2 增加样本
3.3.3 消减样本
3.4 自适应动态无偏最小二乘支持向量机
3.4.1 滑动时间窗长度的自适应选取
3.4.2 算法复杂度分析
3.5 仿真实例和结果分析
3.5.1 混沌时间序列预测
3.5.2 非线性系统在线预测跟踪
3.5.3 二容水箱故障预报
3.6 本章小结

第4章多输出支持向量回归机故障预报模型
4.1 引言
4.2 问题描述
4.3 支持向量回归机
4.4 多输出支持向量回归机
4.4.1 线性模型
4.4.2 偏置求取算法
4.4.3 非线性模型
4.5 多输出最小二乘支持向量机
4.5.1 模型设计
4.5.2 算法性质分析
4.6 仿真实例和结果分析
4.6.1 时间序列多步预测
4.6.2 多输入多输出系统故障预报
4.7 本章小结

第5章支持向量机概率密度估计故障预报模型
5.1 引言
5.2 支持向量机概率密度估计
5.3 算法改进
5.3.1 单松弛因子支持向量机概率密度估计模型
5.3.2 单松弛因子多核支持向量机概率密度估计模型
5.3.3 非耦合数据合成概率密度估计
5.3.4 算法复杂度分析
5.3.5 仿真实例和结果分析
5.4 基于支持向量机概率密度估计的故障预报
5.4.1 问题描述
5.4.2 异常程度的衡量
5.4.3 仿真实例和结果分析
5.5 本章小结

第6章基于支持向量机的惯性器件故障预报
6.1 引言
6.2 基于单维漂移数据的惯性器件故障预报
6.2.1 基于支持向量预选取one-class SVM的故障预报
6.2.2 基于自适应动态无偏LSSVM的故障预报
6.2.3 基于多输出支持向量回归机的故障预报
6.3 某惯性平台系统故障预报
6.3.1 平台系统漂移系数的求取
6.3.2 基于超球体模型的故障预报
6.4 本章小结

第7章复杂系统故障预报新技术展望
7.1 全流程故障预报技术
7.2 多元时序驱动的故障预测和多核机器建模与学习相融合
7.3 多元数据驱动故障预报方法的最新进展
7.3.1 故障预测与可靠性评估方法的最新进展
7.3.2 数据驱动的基于核机器学习的故障预测与可靠性评估方法最新进展
7.4 多元数据驱动的复杂系统全流程故障预报方案设计
参考文献
内容简介

Book Abstract

《复杂系统支持向量机建模与故障预报》：
1.基于卡尔曼（Kalman）滤波器的故障预报方法
Kalman滤波器通过对系统状态估计误差的极小化，得到递推估计的一组方程，由于它同时得到了系统的预报方程，因此应用于故障预报领域。
2.基于强跟踪滤波器的故障预报方法
对于非线性系统，Kalman滤波器失效，其扩展形式虽然可以使用，但鲁棒性较差，而且当系统达到平衡状态时，就会丧失对突变状态的跟踪能力。在这种情况下，以该种滤波器建立的故障预报模型将会导致误报率的上升。为了解决该问题，一种基于强跟踪滤波器的自适应故障预报方法被提出，并解决了一类带时变参数的非线性系统的故障预报问题。这种方法的本质是一种带次优渐消因子的扩展Kalman滤波器，它具有较好的关于模型不确定性的鲁棒性，以及极强的关于突变状态的跟踪能力和适中的计算复杂度。而且，强跟踪滤波器是一种自适应滤波器，它能同时估计状态和时变参数。因此，这种方法能够对具有时变参数的非线性系统进行故障预报。
……

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