Описание: This monograph is the continuation and completion of the monograph, "Intelligent Systems: Approximation by Artificial Neural Networks" written by the same author and published 2011 by Springer.The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks.
Описание: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Описание: The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component.
Автор: Dehuri Satchidananda Et Al Название: Integration Of Swarm Intelligence And Artificial Neural Network ISBN: 9814280143 ISBN-13(EAN): 9789814280143 Издательство: World Scientific Publishing Рейтинг: Цена: 16790.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). This title accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning.
Описание: Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems.
Автор: Anthony, Martin Bartlett, Peter Название: Neural network learning ISBN: 052111862X ISBN-13(EAN): 9780521118620 Издательство: Cambridge Academ Рейтинг: Цена: 7602.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. It is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.
Автор: J.G. Taylor; E.R. Caianiello; R.M.J. Cotterill; J. Название: Neural Network Dynamics ISBN: 3540197710 ISBN-13(EAN): 9783540197713 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The proceedings of a workshop which encompassed a range of topics in which neural networks played a fundamental role. Attempting to bridge the gap between neural computation and computational neuroscience, the papers describe the foundations of neural network dynamics and their applications.
Автор: Frank H. Eeckman Название: Computation in Neurons and Neural Systems ISBN: 0792394658 ISBN-13(EAN): 9780792394655 Издательство: Springer Рейтинг: Цена: 36570.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Contains papers which represent a cross-section of the state-of-the-art research work in the field of computational neuroscience, including coverage of analysis and modelling work, as well as results of new biological experimentation.
This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization.
Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process are presented. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.
Автор: Shigeo Abe Название: Neural Networks and Fuzzy Systems ISBN: 0792398149 ISBN-13(EAN): 9780792398141 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems. This book includes performance comparison of neural networks and fuzzy systems using data gathered from real systems. It covers topics covered like: Hopfield network for combinatorial optimization problems, multilayered neural networks.
Автор: Frank H. Eeckman Название: Analysis and Modeling of Neural Systems ISBN: 0792392175 ISBN-13(EAN): 9780792392170 Издательство: Springer Рейтинг: Цена: 25848.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Yoshiyasu Takefuji Название: Neural Network Parallel Computing ISBN: 079239190X ISBN-13(EAN): 9780792391906 Издательство: Springer Рейтинг: Цена: 28734.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Demonstrates the computational power of neural network computing by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight`s tour, spare allocation, sorting and searching, and tiling.
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