Author by: Kevin L. Priddy Language: en Publisher by: SPIE Press Format Available: PDF, ePub, Mobi Total Read: 80 Total Download: 470 File Size: 50,5 Mb Description: This tutorial text provides the reader with an understanding of artificial neural networks (ANNs) and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks. Author by: P.J.

Braspenning Language: en Publisher by: Springer Science & Business Media Format Available: PDF, ePub, Mobi Total Read: 82 Total Download: 537 File Size: 40,7 Mb Description: This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed, yet clear and illuminating way. Practical experience is provided by discussing several real-world applications in such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM. Author by: Raul Rojas Language: en Publisher by: Springer Science & Business Media Format Available: PDF, ePub, Mobi Total Read: 80 Total Download: 133 File Size: 55,7 Mb Description: Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets.

Introduction to Artificial Neural Systems. Introduction to artificial neural systems jacek m. It explains the acquisition and retrieval of the experimental knowledge in compactly interconnected networks comprising of cells of processing elements and mm.zurada links.

Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing. Author by: Kishan Mehrotra Language: en Publisher by: MIT Press Format Available: PDF, ePub, Mobi Total Read: 35 Total Download: 392 File Size: 46,9 Mb Description: Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them.

Download aui converter 48x44 torrent. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied.

Applications are discussed along with the algorithms. A separate chapter takes up optimization methods.

The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text. Author by: B.