In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features x Explains neural networks in a multi-disciplinary context x Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting x Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA.

Neural Networks - A Comprehensive Foundation

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贝叶斯网络经典教材都涵盖在这里面了，欢迎大家使用~！

Computer Networks Andrew Tanenbaum 英文版第五版 pdf文字版

Wei Ren和Yongcan Cao关于多智能体系统分布式协调控制方向的经典教材。

关于这本书的介绍可以在网上找，书中第二章关于面向对象编程的东西没有扫描，相关内容可以去看更详细的书籍。 添加了书签。 关于资源分：没办法，我也要去下载别人的资源。 共有3个rar。

共3个rar，必须一起下载！没办法，上传限制呀！！说明见part1.

共3个rar，必须一起下载！说明见part1

使用神经网络进行预测，有BF，FF，GRNN，RBF网络等，