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Jingdong book

智能控制技术（修订版）

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Jingdong book

智能控制技术（修订版）

Name: 智能控制技术（修订版）
Brand: Jingdong book
SKU: 3118073711
Price: 41.82 USD
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为了适应21世纪科学技术新发展的需要，体现当代科学发展中多学科间的知识交叉，并结合高等学校人才培养的特点，决定对《智能控制技术》一书进行修订再版。《智能控制技术（修订版）》再版时，在保持原有取材体系风格不变的同时，修改或补充了有关内容。在知识体系结构方面，包括智能控制的基础知识，智能控制器的设计技术，智能控制系统实现技术和智能信息处理方法等几个层次；在内容编排方面，深入浅出，易读易懂，便于自学。

Catalogue

第一章智能控制概述
1．1 智能控制的基本概念
1．1．1 什么是智能控制
1．1．2 智能控制的研究对象
1．2 智能控制系统的特征和性能
1．2．1 智能控制系统的一般结构
1．2．2 智能控制系统的主要功能特征
1．2．3 智能控制系统的特征模型
1．3 智能控制系统的类型
1．4 智能控制的发展概况
1．5 小结
习题
第二章智能控制的知识工程基础
2．1 知识的基本概念
2．1．1 什么是知识
2．1．2 知识的分类
2．2 知识的表示
2．2．1 一阶谓词表示法
2．2．2 时序逻辑表示法
2．2．3 产生式表示法
2．2．4 语义网络知识表示法
2．2．5 框架知识表示法
2．2．6 Petri网知识表示法
2．2．7 定性模型知识表示法
2．2．8 可视知识模型
2．3 知识的获取
2．3．1 非自动知识获取
2．3．2 自动知识获取
2．4 知识的处理
2．4．1 推理的方式与分类
2．4．2 推理控制策略
2．4．3 状态空间的搜索策略
2．5 小结
习题
第三章分级递阶智能控制
3．1 递阶控制的一般原理
3．1．1 大系统递阶结构的描述
3．1．2 递阶控制的一般原理
3．2 分级递阶智能控制
3．2．1 分级递阶智能控制系统的结构
3．2．2 分级递阶智能控制原理
3．3 小结
习题
第四章遗传算法
4．1 什么是遗传算法
4．1．1 遗传算法的生物遗传学基础
4．1．2 遗传算法的特点
4．1．3 遗传算法的基本操作
4．2 遗传算法的理论基础
4．2．1 遗传算法的模式理论
4．2．2 遗传算法实现中的一些基本问题
4．3 基于遗传的机器学习系统
4．3．1 分类器系统的结构
4．3．2 规则信息系统
4．3．3 信任分配系统
4．3．4 机器学习中的遗传算法
4．4 遗传算法的计算机实现
4．5 基于遗传算法的系统在线辨识
4．5．1 遗传算法在参数辨识中的应用
4．5．2 遗传算法参数辨识仿真示例
4．6 小结
习题
第五章神经网络控制
5．1 神经网络的基本概念
5．1．1 生物神经元模型
5．1．2 人工神经元模型
5．1．3 人工神经网络模型
5．1．4 神经网络的学习方法
5．2 前向网络及其主要算法
5．2．1 感知器
5．2．2 BP网络
5．2．3 RBF网络
5．3 反馈网络
5．3．1 Hopfield网络
5．3．2 Boltzmann机网络
5．3．3 自组织特征映射网络（Kohonen网络）
5．4 神经网络模型辨识
5．4．1 正向建模
5．4．2 逆模型
5．5 神经元自适应PID控制
5．5．1 神经控制的基本思想
5．5．2 单神经元自适应PID控制
5．6 神经元自适应PSD控制
5．6．1 自适应PSD控制算法
5．6．2 单神经元自适应PSD控制
5．7 神经网络内模控制
5．7．1 内模控制
5．7．2 神经网络内模控制
5．8 神经网络自适应控制
5．8．1 神经网络自校正控制
5．8．2 神经网络模型参考控制
5．9 神经网络PID控制
5．9．1 基于BP神经网络Kp，KI，KD参数自学习PID控制器
5．9．2 改进型BP神经网络KP，KI，KD参数自学习PID控制器
5．10 小结
习题
第六章模糊控制的数学基础
6．1 概述
6．1．1 模糊概念
6．1．2 模糊性与随机性
6．2 模糊集合
6．2．1 普通集合
6．2．2 模糊集合
6．2．3 模糊集合与普通集合的联系
6．3 模糊关系与模糊关系合成
6．3．1 模糊关系的基本概念
6．3．2 模糊关系合成
6．3．3 模糊关系的性质
6．3．4 模糊变换
6．4 模糊推理
6．4．1 模糊语言与语言变量
6．4．2 模糊命题与模糊条件语句
6．4．3 模糊推理
6．5 小结
习题
第七章模糊控制
7．1 模糊控制系统原理
7．1．1 传统控制系统的特点
7．1．2 模糊控制系统的工作原理
7．1．3 模糊控制的系统结构
7．1．4 模糊控制器的结构与组成
7．2 模糊控制器设计
7．2．1 模糊控制器设计要求
7．2．2 清晰量的模糊化
7．2．3 模糊量的清晰化
7．2．4 模糊控制规则及控制算法
7．3 自调整模糊控制技术
7．3．1 带有自调整因子的模糊控制器
7．3．2 带有自调整函数的模糊控制器
7．4 神经网络实现的模糊控制
7．4．1 常规模糊系统的等价神经网络
7．4．2 模糊神经网络技术在温度控制过程中的应用
7．4．3 基于T—S模型的模糊神经网络
7．5 基于遗传算法优化的模糊控制
7．5．1 遗传算法和模糊逻辑、神经网络的融合
7．5．2 基于遗传算法优化的模糊控制器
7．5．3 基于遗传算法的模糊温度控制实验
7．6 小结
习题
第八章专家控制
8．1 专家系统概述
8．1．1 什么是专家系统
8．1．2 专家系统的基本组成
8．1．3 专家系统的特征及类型
8．2 专家控制系统
8．2．1 专家控制系统的特点
8．2．2 专家控制系统的工作原理
8．2．3 专家控制器
8．3 模糊专家系统
8．3．1 模糊专家系统的基本结构
8．3．2 可能性分布与模糊测度
8．3．3 模糊性知识的规则表示
8．3．4 不确定性推理模型
8．4 逻辑程序设计语言
8．4．1 Prolog语言的特点
8．4．2 Prolog语言的语法与数据结构
8．4．3 Prolog程序的执行与控制
8．5 小结
习题
第九章基于粗糙集和含糊集理论的智能信息处理方法
9．1 粗糙集理论的基本概念
9．1．1 粗糙集理论的数值分析
9．1．2 基于粗糙集理论的规则知识获取
9．1．3 粗糙集知识获取策略在心电图波形识别中的应用
9．2 基于粗糙集理论的模糊神经网络
9．2．1 建立基于输入输出数据的知识表达系统
9．2．2 基于粗糙集理论的模糊神经网络
9．2．3 二维非线性函数图像拟合应用
9．3 含糊集理论的基本概念
9．3．1 基本概念
9．3．2 含糊集的改进性质
9．3．3 实例分析
9．4 基于含糊集的近似推理方法
9．4．1 含糊集的合成推理
9．4．2 含糊集插值推理
9．4．3 几种含糊集近似推理方法的比较
9．5 一种含糊一粗糙集模型
9．6 含糊集知识获取策略
9．6．1 含糊集决策表知识表达系统
9．6．2 含糊集知识获取策略
9．7 小结
习题
第十章智能控制应用示例
10．1 电加热炉炉温智能控制
10．1．1 电加热炉模型分析
10．1．2 电加热炉炉温智能控制
10．2 集装箱吊车的模糊控制
10．2．1 吊车模糊控制规则的建立
10．2．2 模糊逻辑吊车控制器的结构
10．2．3 模糊控制的可编程控制器实现
10．3 模糊控制技术的微机实现
10．3．1 MC68HCllE9数字单片机的特性
10．3．2 温度模糊控制器的实现
10．4 模糊控制的洗衣机
10．4．1 模糊控制洗衣机系统电路结构
10．4．2 洗衣机的模糊推理
10．4．3 洗衣机物理量检测方法
10．4．4 布质、布量的模糊推理
10．5 倒立摆的模糊神经网络控制
10．5．1 再励学习的模糊神经网络
10．5．2 倒立摆的模糊神经网络自适应控制
附录1 改进的BP神经网络及其MATLAB仿真
附1．1 提高BP神经网络收敛速度的方法
附1．2 提高神经网络收敛速度的一种赋初值算法
附1．3 BP网络训练技巧
附1．4 感知器分类的MATLAB仿真
附1．5 具有噪声二阶系统辨识的MATLAB程序仿真
附1．6 多维非线性辨识的MATLAB程序剖析
附录2 模糊控制MATLAB程序仿真示例
附录3 基于粗糙集可辨识矩阵属性简约算法软件
附录4 基于Vague集可辨识矩阵知识获取算法软件
参考文献

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