Автор: Shigeo Abe Название: Neural Networks and Fuzzy Systems ISBN: 1461378699 ISBN-13(EAN): 9781461378693 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Neural Networks and Fuzzy Systems: Theory and Applications discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems.
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.
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.
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.
Автор: Brian D. Ripley Название: Pattern Recognition and Neural Networks ISBN: 0521717701 ISBN-13(EAN): 9780521717700 Издательство: Cambridge Academ Рейтинг: Цена: 7762.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from www.stats.ox.ac.uk/~ripley/PRbook/
Автор: Rafik Aziz Aliev; Babek Ghalib Guirimov Название: Type-2 Fuzzy Neural Networks and Their Applications ISBN: 3319381598 ISBN-13(EAN): 9783319381596 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing.
Автор: Skorohod Boris. A Название: Diffuse Algorithms for Neural and Neuro-Fuzzy Networks ISBN: 0128126094 ISBN-13(EAN): 9780128126097 Издательство: Elsevier Science Рейтинг: Цена: 15159.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new approaches to training neural and neuro-fuzzy networks. This book is divided into six chapters. Chapter 1 consists of plants models reviews, problems statements, and known results that are relevant to the subject matter of this book. Chapter 2 considers the RLS behavior on a finite interval. The theoretical results are illustrated by examples of solving problems of identification, control, and signal processing.
Properties of the bias, the matrix of second-order moments and the normalized average squared error of the RLS algorithm on a finite time interval are studied in Chapter 3. Chapter 4 deals with the problem of multilayer neural and neuro-fuzzy networks training with simultaneous estimation of the hidden and output layers parameters. The theoretical results are illustrated with the examples of pattern recognition, identification of nonlinear static, and dynamic plants.
Chapter 5 considers the estimation problem of the state and the parameters of the discrete dynamic plants in the absence of a priori statistical information about initial conditions or its incompletion. The Kalman filter and the extended Kalman filter diffuse analogues are obtained. Finally, Chapter 6 provides examples of the use of diffuse algorithms for solving problems in various engineering applications. This book is ideal for researchers and graduate students in control, signal processing, and machine learning.
Автор: Bishop, Christopher M. Название: Neural Networks for Pattern Recognition ISBN: 0198538642 ISBN-13(EAN): 9780198538646 Издательство: Oxford Academ Рейтинг: Цена: 13939.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books.
In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights.
The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method.
The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ?=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.
The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error.
Автор: Erdal Kayacan Название: Fuzzy Neural Networks for Real Time Control Applications ISBN: 0128026871 ISBN-13(EAN): 9780128026878 Издательство: Elsevier Science Рейтинг: Цена: 12294.00 р. Наличие на складе: Поставка под заказ.
Описание:
AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS
Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book
Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book.
A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis.
You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are:
- Gradient descent
- Levenberg-Marquardt
- Extended Kalman filter
In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced.
The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully.
Автор: Rafik Aziz Aliev; Babek Ghalib Guirimov Название: Type-2 Fuzzy Neural Networks and Their Applications ISBN: 3319090712 ISBN-13(EAN): 9783319090719 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing.
Автор: Kavian & Ghassemlooy Название: Intelligent Systems For Optical Networks Design ISBN: 1466636521 ISBN-13(EAN): 9781466636521 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: As the increased demand for high-speed communication creates an interest in the development of optical networks, intelligent all optical networks have emerged as the next generation for reliable and fast connections. <br><br><em>Intelligent Systems for Optical Networks Design: Advancing Techniques</em> is a comprehensive collection of research focused on theoretical and practical aspects of intelligent methodologies as applied to real world problems. This reference source is useful for research and development engineers, scholars, and students interested in the latest development in the area of intelligent systems for optical networks design.
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