电力电子系统建模及控制（徐德鸿）

说明：  本书共有43章，内容涵盖常见的神经网络（BP、RBF、SOM、Hopfield、Elman、LVQ、Kohonen、GRNN、NARX等）以及相关智能算法（SVM、决策树、随机森林、极限学习机等）。同时，部分章节也涉及了常见的优化算法（遗传算法、蚁群算法等）与神经网络的结合问题。此外，本书还介绍了MATLAB R2012b中神经网络工具箱的新增功能与特性，如神经网络并行计算、定制神经网络、神经网络高效编程等(This book consists of 43 chapters, covering common neural networks (BP, RBF, SOM, Hopfield, Elman, LVQ, Kohonen, GRNN, NARX, etc.) and related intelligent algorithms (SVM, decision tree, random forest, extreme learning machine, etc.). At the same time, some chapters also involve the combination of common optimization algorithms (genetic algorithm, ant colony algorithm, etc.) and neural networks. In addition, the book also introduces the new functions and characteristics of the neural network toolbox in MATLAB R2012b, such as parallel computing of neural networks, customized neural networks, efficient programming of neural networks, etc)

Cruise高级培训教程，对熟悉和了解Cruise仿真有很大的帮助。(Cruise advanced training course is very helpful to familiarize with and understand Cruise simulation.)

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王道论坛的考研上机指南，对于算法初学者是很好的资料。(a guidance to coding of wangdao bbs,which can help algorithm learner)

说明：  一本关于ＵＮＩＸ下编程的书籍 很有价值的(a programming under UNIX on the books of great value)

本书主要介绍模型预测控制理论与方法在无人驾驶车辆路径规划与跟踪控制方面的基础应用技术。由于模型预测控制理论数学抽象特点明显，初涉者往往需要较长时间的探索才能真正理解和掌握，进一步应用到具体研究，则需要更长的过程。(This book mainly introduces the basic application technology of model predictive control theory and method in unmanned vehicle routing planning and tracking control. Because of the obvious abstract mathematical characteristics of model predictive control theory, it often takes a long time for the novice to really understand and master. Further application to specific research requires a longer process.)

说明：  本书由Keras之父、现任Google A工智能研究员的弗朗索瓦?肖莱（Frangois Chollet）执笔，详尽介 绍了用Python和Keras进行深度学习的探索实践，涉及计算机视觉、自然语言处理、生成式模型等应用。 书中包含30多个代码示例，步骤讲解详细透彻。由于本书立足于人工智能的可达性和大众化，读者无须 具备机器学习相关背景知识即可展开阅读。在学习完本书后，读者将具备搭建自己的深度学习环境、建立 图像识别模型、生成图像和文字等能力(This book is written by Frangois Chollet, the father of keras and the current researcher of Google a intelligence. It introduces in detail the exploration and practice of deep learning with Python and keras, involving computer vision, natural language processing, generative model and other applications. The book contains more than 30 code examples, and the steps are detailed and thorough. Because this book is based on the accessibility and popularization of artificial intelligence, readers can read it without having the background knowledge of machine learning. After learning this book, readers will have the ability to build their own deep learning environment, establish image recognition model, and generate images and characters)

GA工具箱,内有详细的说明文档,我找了很长时间才找到的。(GA toolbox for matlab.)

说明：  DEA solver for windows 10 in matlab666

说明：  this is a nice math formating book: intro to latex, tutorial