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工业人工智能概论

作译者：韩英华等

出版时间：2020-01 千字数：196 版次：01-01 页数：164

开本：16开装帧： I S B N ：9787121381621

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内容简介

前言

上架建议

作者简介

编辑推荐

音视频专区

第 1 章 绪论·····················································································1 
1.1 人工智能的内涵及发展····································································1 
1.2 工业人工智能的内涵及发展······························································4 
1.2.1 工业人工智能的发展背景 ·····························································4 
1.2.2 工业人工智能的发展历程 ·····························································6 
1.2.3 工业人工智能与传统人工智能的区别················································7 
1.3 工业人工智能的算法功能和典型应用··················································8 
1.4 工业人工智能的知识体系································································11 
1.5 工业人工智能的关键技术································································12 
第 2 章 数据分析与数据挖掘工具··························································14 
2.1 工业大数据的特点··········································································15 
2.2 工业大数据挖掘的定义及过程···························································17 
2.3 工业数据的特征分析·······································································18 
2.3.1 工业数据的统计描述 ···································································18 
2.3.2 工业数据的相关性分析 ································································20 
2.4 工业数据的预处理··········································································23 
2.4.1 异常值处理 ···············································································23 
      工业数据异常值的处理案例 ·····························································25 
2.4.2 缺失值处理 ···············································································26 
      工业数据缺失值的处理案例 ·····························································27 
2.4.3 数据归一化处理 ·········································································28 
2.4.4 不等时距数据处理 ······································································28
      工业数据不等时距处理案例·····························································.29 
2.4.5 离群点检测···············································································.32 
2.4.6 不平衡数据集检测······································································.34 
2.4.7 数据降维处理············································································.36 
2.5 假设检验····················································································.41 
2.5.1 假设检验的基本原理···································································.41 
2.5.2 假设检验的一般步骤···································································.42 
2.5.3 假设检验的种类·········································································.42 
2.6 多变量分析模型 ··········································································.44 
2.6.1 线性模型··················································································.44 
2.6.2 广义线性模型············································································.45 
2.6.3 线性混合效应模型······································································.47 
2.7 最大似然估计··············································································.48 
2.8 关联分析····················································································.49 
2.8.1 关联规则挖掘···········································································.·51 
2.8.2 关联规则算法···········································································.·52 
2.8.3 时序关联规则算法······································································.54 
2.8.4 一维时序关联规则······································································.56 
2.8.5 多维时序关联规则······················································.··············.59 
2.9 数据分析系统的体系架构 ··············································.··············.64 
第 3 章 机器学习 ····························································.···············.65 
3.1 人类的思考过程与机器学习的过程 ···················································.65 
3.2 机器学习的知识体系 ···················································.················.67 
3.3 Python 语言的优势·········································································.68 
3.4 工业人工智能的有监督学习算法 ·······················································.68 
3.4.1 决策树算法···············································································.··70 
          决策树算法的应用案例：判断影响打网球的相关因素····························73 
3.4.2 回归算法·····················································································75 
3.4.3 支持向量机算法 ··········································································80 
有监督学习算法的工业应用案例······························································86 
3.5 工业人工智能的无监督学习算法·························································87 
3.5.1 聚类算法 ···················································································87 
3.5.2 自动编码器 ················································································94 
无监督学习算法的工业应用案例·······························································95 
3.6 机器学习的主要挑战及应对方法··························································97 
3.6.1 机器学习的主要挑战 ·····································································97 
3.6.2 应对挑战的主要方法 ····································································100 
第 4 章 深度学习········································································.·······103 
4.1 深度学习的核心·····································································.········103 
4.2 深度学习的常用网络：卷积神经网络····················································106 
4.2.1 卷积神经网络的结构 ·····································································107 
4.2.2 卷积神经网络的关键技术 ·······························································109 
4.2.3 卷积神经网络的训练 ·····································································112 
          卷积神经网络的工业应用案例························.·································113 
4.3 深度学习的常用网络：循环神经网络··················.································115 
4.3.1 循环神经网络的结构 ····································. ·······························115 
4.3.2 循环神经网络的优化 ·····································································117 
          循环神经网络的工业应用案例··························································120 
4.4 深度学习的常用网络：受限玻尔兹曼机·················································126 
4.4.1 受限玻尔兹曼机的分类 ··································································126 
4.4.2 受限玻尔兹曼机的训练 ··································································131 
4.5 深度学习的常用网络：深度置信网络····················································134 
4.5.1 深度置信网络的构建 ·····································································134 
4.5.2 深度置信网络的训练 ·····································································135 
          深度置信网络的工业应用案例··························································137 
4.6 深度学习的常用框架·········································································143 
4.6.1 Theano·····················································································.143 
4.6.2 TensorFlow··············································································.  144 
4.6.3 Keras ·······················································································.146 
4.6.4 Caffe/Caffe2···········································································.    ·146 
4.6.5 MXNet ····················································································.   147 
4.6.6 CNTK······················································································.  148 
第 5 章 工业人工智能面临的挑战 ···························································149 
5.1 工业人工智能带来的变革 ·································································.149 
5.2 工业人工智能面临的挑战 ·································································.150 
参考文献·······························································································153

人工智能的概念从1956年第一次被提出至今，历经数次起伏，已不再是一个新概念。随着深度学习神经网络取得突破性进展，人工智能再次迎来发展的黄金期。 
如今，人工智能在社交、商业等领域的应用，已经产生了深远的影响和切实的价值。这些应用为人工智能在工业领域的落地所建立的基础，正在成为未来实现工业人工智能的潜力。当前，全球工业正处于向工业 4.0 阶段的转型期，以价值创造为目标和驱动力，为人工智能的发展与行业应用提供了合适的土壤。随着新一轮信息革命与产业变革的蓬勃兴起，工业智能化发展正在成为全球的关注重点。世界各国高度重视，积极出台相关战略、政策，促进人工智能在工业领域的应用、发展。 
笔者多年来一直从事自动化技术的研究工作，并致力于人工智能技术在工业中的应用研究，探索新一代人工智能技术在智能制造领域所发挥的作用。 
在国家重点研发计划项目（扁平材全流程智能化制备关键技术，编号：2017YFB0304100）和国家自然科学基金重点项目（基于大规模数据驱动的轧钢过程故障诊断及自愈控制，编号：51634002）的资助下，作者将近几年对人工智能技术在工业应用的研究成果总结成册，目的是为工业人工智能的初学者提供入门教材，为技术专业人员提供有价值的参考和帮助，希望读者通过此书能够加深对工业人工智能的理解，了解工业人工智能的基本技术和方法，掌握一些工业人工智能实际应用的解决方案。 
本书可作为相关专业的本科生及研究生学习工业人工智能的入门教材，也可作为企业专业技术人员了解工业人工智能的培训教材和参考书，还可作为对工业人工智能感兴趣的各界人士的普及性读物。 
本书在成稿过程中，参考了大量的国内外研究资料，在此一并表示感谢。书中如有不妥之处，希望读者批评指正。 
作 者 
2019年12月

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