Описание: Collects together most of the papers presented at the Twelfth Neural Computation and Psychology Workshop (NCPW12) held in 2010 at Birkbeck College (England). This book covers a wide range of research topics in neural computation and psychology, including cognitive development, language processing, higher-level cognition, and more.
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: Kutz J Nathan Название: Data-Driven Modeling & Scientific Computation ISBN: 0199660344 ISBN-13(EAN): 9780199660346 Издательство: Oxford Academ Рейтинг: Цена: 7443.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.
Автор: Bianchini Monica Название: Handbook on Neural Information Processing ISBN: 3642366562 ISBN-13(EAN): 9783642366567 Издательство: Springer Рейтинг: Цена: 26122.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Licheng Jiao; Lipo Wang; Xinbo Gao; Jing Liu; Feng Название: Advances in Natural Computation ISBN: 3540459014 ISBN-13(EAN): 9783540459019 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the proceedings of the Second International Conference on Natural Computation, ICNC 2006. This first volume includes 130 papers related to artificial neural networks, natural neural systems and cognitive science, neural network applications, as well as evolutionary computation: theory and algorithms.
Описание: Constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain in April 2007, colocated with the Evo 2007 events. This book presents 28 revised full papers that were reviewed and selected from 60 submissions.
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