Nonlinear control systems using MATLAB 非线性系统控制MATLAB This book introduces nonlinear control systems for control engi- neering and science to graduate, undergraduate students and re- searchers; it targets control engineering students who do not like to do not have time to derive and prove mathematical results for nonlinear control systems. It can be serve as a text book for nonlin- ear control systems, especially for feedback linearization techniques which is a common approach in controlling nonlinear systems

Digital Logic Design Using Verilog Coding and RTL Synthesis.bak

这里面包含Numerical Methods Using MATLAB_Mathews_4th中英和答案pdf，需要的同学可以拿去，帮助更多学习算法的同学

图像去雾算法的的原文，在本文中，我们提出了一个简单但有效的图像先验暗通道之前，从一个单一的输入图像去除混浊。暗通道先验是一种室外无雾图像的统计。这是基于一个关键的观察结果，大多数室外无雾图像中的局部斑点包含一些像素，这些像素在至少一个颜色通道中的强度非常低。利用这一先验模型，我们可以直接估计薄雾的厚度，并恢复高质量的无薄雾图像。在各种模糊图像上的结果表明了该算法的有效性。此外，作为除雾的副产品，还可以获得高质量的深度图。

全名: data structures and problem solving using java

这是关于可视化算法的电子书，高清，最新版本，经典著作，英文版

许多行业专家认为，无人监督学习人工智能的下一个前沿，这可能是人工智能研究的关键，即所谓的一般人工智能。由于世界上大多数数据都没有标记，因此无法应用传统的监督学习;这就是无监督学习的用武之地。无监督学习可以应用于未标记的数据集，以发现埋藏在数据深处的有意义的模式，人类几乎不可能发现这些模式。 作者Ankur Patel使用两个简单的，生产就绪的Python框架 - scikit-learn和使用Keras的TensorFlow，提供了有关如何应用无监督学习的实用知识。通过提供实际操作示例和代码，您将识别难以发现的数据模式，获得更深入的业务洞察力，检测异常，执行自动特征工程和选择，以及生成合成数据集。您只需要编程和一些机器学习经验即可开始使用。

About This Book Build your own low-power, wireless network using ready-made Arduino and XBee hardware Create a complex project using the Arduino prototyping platform A guide that explains the concepts and builds upon them with the help of examples to form projects Who This Book Is For This book is targeted at embedded system developers and hobbyists who have some working knowledge of Arduino and who wish to extend their projects using wireless connectivity.

Categorical Data Analysis Using SAS(3rd) 英文无水印原版pdf 第3版 pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Machine rule induction was examined on a difficult categorization problem by applying a Hollandstyle classifier system to a complex letter recognition task. A set of 20,000 unique letter images was generated by randomly distorting pixel images of the 26 uppercase letters from 20 different commercial fonts. The parent fonts represented a full range of character types including script, italic, serif, and Gothic. The features of each of the 20,000 characters were summarized in terms of 16 primitive numerical attributes. Our research focused on machine induction techniques for generating IF-THEN classifiers in which the IF part was a list of values for each of the 16 attributes and the THEN part was the correct category, i.e., one of the 26 letters of the alphabet. We examined the effects of different procedures for encoding attributes, deriving new rules, and apportioning credit among the rules. Binary and Gray-code attribute encodings that required exact matches for rule activation were compared with integer representations that employed fuzzy matching for rule activation. Random and genetic methods for rule creation were compared with instance-based generalization. The strength/specificity method for credit apportionment was compared with a procedure we call "accuracy/utility."