Описание: This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems.
Автор: Dikai Liu; Lingfeng Wang; Kay Chen Tan (Eds.) Название: Design and Control of Intelligent Robotic Systems ISBN: 3540899324 ISBN-13(EAN): 9783540899327 Издательство: Springer Рейтинг: Цена: 36570.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With the increasing applications of intelligent robotic systems in various ?elds, the - sign and control of these systems have increasingly attracted interest from researchers.
Автор: M. Teshnehlab; Watanabe Kyoko Название: Intelligent Control Based on Flexible Neural Networks ISBN: 9048152070 ISBN-13(EAN): 9789048152070 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems.
Автор: Shuai Li; Yinyan Zhang Название: Neural Networks for Cooperative Control of Multiple Robot Arms ISBN: 9811070369 ISBN-13(EAN): 9789811070365 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples.
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: Duc T. Pham; Xing Liu Название: Neural Networks for Identification, Prediction and Control ISBN: 1447132467 ISBN-13(EAN): 9781447132462 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network.
Автор: George A. Rovithakis; Manolis A. Christodoulou Название: Adaptive Control with Recurrent High-order Neural Networks ISBN: 1447112016 ISBN-13(EAN): 9781447112013 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ...
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: 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.
Описание: With the goal of alleviating the paucity of knowledge about advanced dementia, and helping to improve the care and services that are increasingly needed for the growing numbers of people living with dementia-type diseases, this book provides evidence-based measurement scales for use by researchers and care providers who are seeking to improve our understanding of the final stages of this disease.
Автор: Khalid Karam Abd Название: Intelligent Scheduling of Robotic Flexible Assembly Cells ISBN: 3319387367 ISBN-13(EAN): 9783319387369 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Background and Research Scope.- Literature Review and Research Objectives.- Development of an Intelligent Methodology for Scheduling RFAC.- Case Study 1: Application of the Developed Methodology Using Fuzzy Logic and Simulation.- Simulation Modelling and Analysis of Dynamic Scheduling in RFAC.- Development of an Optimization Approach for Dynamic Scheduling Problems in RFAC.- Case Study 2: Application of Hybrid Fuzzy MCDM Approach to Optimize Dynamic Scheduling in RFAC.- Conclusions and Recommendations for Further Work.
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