Static and Dynamic Neural Networks

Static and Dynamic Neural Networks
Author: Madan Gupta
Publisher: John Wiley & Sons
Total Pages: 752
Release: 2004-04-05
Genre: Computers
ISBN: 0471460923

Neuronale Netze haben sich in vielen Bereichen der Informatik und künstlichen Intelligenz, der Robotik, Prozeßsteuerung und Entscheidungsfindung bewährt. Um solche Netze für immer komplexere Aufgaben entwickeln zu können, benötigen Sie solide Kenntnisse der Theorie statischer und dynamischer neuronaler Netze. Aneignen können Sie sie sich mit diesem Lehrbuch! Alle theoretischen Konzepte sind in anschaulicher Weise mit praktischen Anwendungen verknüpft. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen.

Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications

Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications
Author: Long Jin
Publisher: Frontiers Media SA
Total Pages: 301
Release: 2024-07-24
Genre: Science
ISBN: 2832552013

Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.

Fundamentals of Artificial Neural Networks

Fundamentals of Artificial Neural Networks
Author: Mohamad H. Hassoun
Publisher: MIT Press
Total Pages: 546
Release: 1995
Genre: Computers
ISBN: 9780262082396

A systematic account of artificial neural network paradigms that identifies fundamental concepts and major methodologies. Important results are integrated into the text in order to explain a wide range of existing empirical observations and commonly used heuristics.

The Neurobiology of Neural Networks

The Neurobiology of Neural Networks
Author: Daniel Gardner
Publisher: MIT Press
Total Pages: 254
Release: 1993
Genre: Computers
ISBN: 9780262071505

This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks.

Robust and Fault-Tolerant Control

Robust and Fault-Tolerant Control
Author: Krzysztof Patan
Publisher: Springer
Total Pages: 231
Release: 2019-03-16
Genre: Technology & Engineering
ISBN: 303011869X

Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.

Advances in Neural Networks - ISNN 2006

Advances in Neural Networks - ISNN 2006
Author: Jun Wang
Publisher: Springer Science & Business Media
Total Pages: 1429
Release: 2006-05-11
Genre: Computers
ISBN: 3540344829

This is Volume III of a three volume set constituting the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006. 616 revised papers are organized in topical sections on neurobiological analysis, theoretical analysis, neurodynamic optimization, learning algorithms, model design, kernel methods, data preprocessing, pattern classification, computer vision, image and signal processing, system modeling, robotic systems, transportation systems, communication networks, information security, fault detection, financial analysis, bioinformatics, biomedical and industrial applications, and more.

Introduction to Neural Networks with Java

Introduction to Neural Networks with Java
Author: Jeff Heaton
Publisher: Heaton Research Incorporated
Total Pages: 380
Release: 2005
Genre: Computers
ISBN: 097732060X

In addition to showing the programmer how to construct Neural Networks, the book discusses the Java Object Oriented Neural Engine (JOONE), a free open source Java neural engine. (Computers)

Differential Neural Networks for Robust Nonlinear Control

Differential Neural Networks for Robust Nonlinear Control
Author: Alexander S. Poznyak
Publisher: World Scientific
Total Pages: 455
Release: 2001
Genre: Computers
ISBN: 9810246242

This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.).

Engineering Applications of Neural Networks

Engineering Applications of Neural Networks
Author: Dominic Palmer-Brown
Publisher: Springer Science & Business Media
Total Pages: 508
Release: 2009-08-19
Genre: Computers
ISBN: 3642039693

A cursory glance at the table of contents of EANN 2009 reveals the am- ing range of neural network and related applications. A random but revealing sample includes: reducing urban concentration, entropy topography in epil- tic electroencephalography, phytoplanktonic species recognition, revealing the structure of childhood abdominal pain data, robot control, discriminating angry and happy facial expressions, ?ood forecasting, and assessing credit worthiness. The diverse nature of applications demonstrates the vitality of neural comp- ing and related soft computing approaches, and their relevance to many key contemporary technological challenges. It also illustrates the value of EANN in bringing together a broad spectrum of delegates from across the world to learn from each other’s related methods. Variations and extensions of many methods are well represented in the proceedings, ranging from support vector machines, fuzzy reasoning, and Bayesian methods to snap-drift and spiking neurons. This year EANN accepted approximately 40% of submitted papers for fu- length presentation at the conference. All members of the Program Committee were asked to participate in the reviewing process. The standard of submissions was high, according to the reviewers, who did an excellent job. The Program and Organizing Committees thank them. Approximately 20% of submitted - pers will be chosen, the best according to the reviews, to be extended and - viewedagainfor inclusionin a specialissueofthe journalNeural Computing and Applications. We hope that these proceedings will help to stimulate further research and development of new applications and modes of neural computing.