Mathematical Models of Feedback Neural Networks PDF Download

Author: Daniela Zaharie
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 160

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Book Description

Author: Young Ho Kim
Publisher: World Scientific
ISBN: 9789810233761
Category : Computers
Languages : en
Pages : 232

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Book Description
Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively "add intelligence" to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty. This book bridges the gap between feedback control and AI. It provides design techniques for "high-level" neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including "dynamic output feedback", "reinforcement learning" and "optimal design", as well as a "fuzzy-logic reinforcement" controller. The control topologies areintuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.

Author: Rafael Yuste
Publisher: Columbia University Press
ISBN: 0231546653
Category : Science
Languages : en
Pages : 482

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Book Description
The human brain is perhaps the most intricate and fascinating object in the known universe. Through a mysterious process, the activity of billions of neurons within a few pounds of matter generates the unfathomable complexity of the mind. This book is a conversational and accessible introduction to the brain. Beginning from basic elements of neuroscience, the acclaimed scientist Rafael Yuste guides readers through increasingly sophisticated topics, developing a unified framework for how the brain functions. He describes how the brain is organized and how it develops, how neurons operate and form neural circuits, and how these circuits function as neural networks to generate behavior and mental states. Yuste challenges the traditional view that the brain is an input-output machine that reacts reflexively to sensory stimuli. Instead, he argues, the purpose of the brain is to make a predictive model of the world in order to anticipate the future and choose successful courses of action. He gives readers insight into the workings of sensory and motor systems and the neurobiological basis of our perceptions, thoughts, emotions, memories, and consciousness. Peppered with anecdotes and illustrated with elegant drawings and diagrams, this succinct and cohesive book is accessible to readers without previous background in the subject. It is written for anyone seeking to grasp the core principles of neuroscience or looking for a fresh and clear perspective on how the brain works.

Author: Dmitriy Tarkhov
Publisher: Academic Press
ISBN: 012815652X
Category : Science
Languages : en
Pages : 290

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Book Description
Semi-empirical Neural Network Modeling presents a new approach on how to quickly construct an accurate, multilayered neural network solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single-layered neural networks built on the finite element method (FEM). The strength of the new method presented in this book is the automatic inclusion of task parameters in the final solution formula, which eliminates the need for repeated problem-solving. This is especially important for constructing individual models with unique features. The book illustrates key concepts through a large number of specific problems, both hypothetical models and practical interest. - Offers a new approach to neural networks using a unified simulation model at all stages of design and operation - Illustrates this new approach with numerous concrete examples throughout the book - Presents the methodology in separate and clearly-defined stages

Author: Derong Liu
Publisher: Springer
ISBN: 3540723838
Category : Computers
Languages : en
Pages : 1390

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Book Description
This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.

Author: Xingui He
Publisher: Springer Science & Business Media
ISBN: 3540737626
Category : Computers
Languages : en
Pages : 240

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Book Description
For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.

Author: Young Ho Kim
Publisher: World Scientific
ISBN: 9814496456
Category : Technology & Engineering
Languages : en
Pages : 228

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Book Description
Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively “add intelligence” to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for “high-level” neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including “dynamic output feedback”, “reinforcement learning” and “optimal design”, as well as a “fuzzy-logic reinforcement” controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.

Author: Zhenyu Du
Publisher: Springer Science & Business Media
ISBN: 3642330304
Category : Technology & Engineering
Languages : en
Pages : 706

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Book Description
This volume contains the proceedings of the 2012 International Conference of Modern Computer Science and Applications (MCSA 2012) which was held on September 8, 2012 in Wuhan, China. The MCSA 2012 provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of modern computer science and applications in theoretical and practical aspects.

Author: Mohd. Samar Ansari
Publisher: Springer
ISBN: 813221563X
Category : Technology & Engineering
Languages : en
Pages : 217

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Book Description
This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

Author: Raul Rojas
Publisher: Springer Science & Business Media
ISBN: 3642610684
Category : Computers
Languages : en
Pages : 511

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Book 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. 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.