Neural Networks For Nonlinear Programming
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Neural Networks For Nonlinear Programming
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Author : Chia-Yiu Maa
language : en
Publisher:
Release Date : 1991
Neural Networks For Nonlinear Programming written by Chia-Yiu Maa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Mathematical optimization categories.
Advances In Neural Networks Isnn 2011
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Author : Derong Liu
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-10
Advances In Neural Networks Isnn 2011 written by Derong Liu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-10 with Computers categories.
The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011. The total of 215 papers presented in all three volumes were carefully reviewed and selected from 651 submissions. The contributions are structured in topical sections on computational neuroscience and cognitive science; neurodynamics and complex systems; stability and convergence analysis; neural network models; supervised learning and unsupervised learning; kernel methods and support vector machines; mixture models and clustering; visual perception and pattern recognition; motion, tracking and object recognition; natural scene analysis and speech recognition; neuromorphic hardware, fuzzy neural networks and robotics; multi-agent systems and adaptive dynamic programming; reinforcement learning and decision making; action and motor control; adaptive and hybrid intelligent systems; neuroinformatics and bioinformatics; information retrieval; data mining and knowledge discovery; and natural language processing.
Neural Networks In Optimization
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Author : Xiang-Sun Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Neural Networks In Optimization written by Xiang-Sun Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Science categories.
People are facing more and more NP-complete or NP-hard problems of a combinatorial nature and of a continuous nature in economic, military and management practice. There are two ways in which one can enhance the efficiency of searching for the solutions of these problems. The first is to improve the speed and memory capacity of hardware. We all have witnessed the computer industry's amazing achievements with hardware and software developments over the last twenty years. On one hand many computers, bought only a few years ago, are being sent to elementary schools for children to learn the ABC's of computing. On the other hand, with economic, scientific and military developments, it seems that the increase of intricacy and the size of newly arising problems have no end. We all realize then that the second way, to design good algorithms, will definitely compensate for the hardware limitations in the case of complicated problems. It is the collective and parallel computation property of artificial neural net works that has activated the enthusiasm of researchers in the field of computer science and applied mathematics. It is hard to say that artificial neural networks are solvers of the above-mentioned dilemma, but at least they throw some new light on the difficulties we face. We not only anticipate that there will be neural computers with intelligence but we also believe that the research results of artificial neural networks might lead to new algorithms on von Neumann's computers.
Advances In Neural Networks Isnn 2007
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Author : Derong Liu
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-16
Advances In Neural Networks Isnn 2007 written by Derong Liu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-16 with Computers categories.
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.
Nonlinear Programming With Feedforward Neural Networks
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Author :
language : en
Publisher:
Release Date : 1999
Nonlinear Programming With Feedforward Neural Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.
Neural Networks In Optimization
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Author : Xiang-Sun Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-10-31
Neural Networks In Optimization written by Xiang-Sun Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-10-31 with Business & Economics categories.
The book consists of three parts. The first part introduces concepts and algorithms in optimization theory, which have been used in neural network research. The second part covers main neural network models and their theoretical analysis. The third part of the book introduces various neural network models for solving nonlinear programming problems and combinatorial optimization problems. Audience: Graduate students and researchers who are interested in the intersection of optimization theory and artificial neural networks. The book is appropriate for graduate courses.
Artificial Neural Networks
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Author : Edgar Sánchez-Sinencio
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1992
Artificial Neural Networks written by Edgar Sánchez-Sinencio and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.
Automatic Generation Of Algorithms
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Author : Victor Parada
language : en
Publisher: CRC Press
Release Date : 2025-02-10
Automatic Generation Of Algorithms written by Victor Parada and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-10 with Computers categories.
In the rapidly evolving domain of computational problem-solving, this book delves into the cutting-edge Automatic Generation of Algorithms (AGA) paradigm, a groundbreaking approach poised to redefine algorithm design for optimization problems. Spanning combinatorial optimization, machine learning, genetic programming, and beyond, it investigates AGA's transformative capabilities across diverse application areas. The book initiates by introducing fundamental combinatorial optimization concepts and NPhardness significance, laying the foundation for understanding AGA's necessity and potential. It then scrutinizes the pivotal Master Problem concept in AGA and the art of modeling for algorithm generation. The exploration progresses with integrating genetic programming and synergizing AGA with evolutionary computing. Subsequent chapters delve into the AGA-machine learning intersection, highlighting their shared optimization foundation while contrasting divergent objectives. The automatic generation of metaheuristics is examined, aiming to develop versatile algorithmic frameworks adaptable to various optimization problems. Furthermore, the book explores applying reinforcement learning techniques to automatic algorithm generation. Throughout, it invites readers to reimagine algorithmic design boundaries, offering insights into AGA's conceptual underpinnings, practical applications, and future directions, serving as an invitation for researchers, practitioners, and enthusiasts in computer science, operations research, artificial intelligence, and beyond to embark on a journey toward computational excellence where algorithms are born, evolved, and adapted to meet ever-changing real-world problem landscapes.
Neural Networks For Optimization And Signal Processing
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Author : Andrzej Cichocki
language : en
Publisher: John Wiley & Sons
Release Date : 1993-06-07
Neural Networks For Optimization And Signal Processing written by Andrzej Cichocki and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-06-07 with Computers categories.
A topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.
Nonlinear Optimization Approaches For Training Neural Networks
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Author : George Magoulas
language : en
Publisher: Springer
Release Date : 2015-12-24
Nonlinear Optimization Approaches For Training Neural Networks written by George Magoulas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-24 with Mathematics categories.
This book examines how nonlinear optimization techniques can be applied to training and testing neural networks. It includes both well-known and recently-developed network training methods including deterministic nonlinear optimization methods, stochastic nonlinear optimization methods, and advanced training schemes which combine both deterministic and stochastic components. The convergence analysis and convergence proofs of these techniques are presented as well as real applications of neural networks in areas such as pattern classification, bioinformatics, biomedicine, and finance. Nonlinear optimization methods are applied extensively in the design of training protocols for artificial neural networks used in industry and academia. Such techniques allow for the implementation of dynamic unsupervised neural network training without requiring the fine tuning of several heuristic parameters. "Nonlinear Optimization Approaches for Training Neural Networks" is a response to the growing demand for innovations in this area of research. This monograph presents a wide range of approaches to neural networks training providing theoretical justification for network behavior based on the theory of nonlinear optimization. It presents training algorithms, and theoretical results on their convergence and implementations through pseudocode. This approach offers the reader an explanation of the performance of the various methods, and a better understanding of the individual characteristics of the various methods, their differences/advantages and interrelationships. This improved perspective allows the reader to choose the best network training method without spending too much effort configuring highly sensitive heuristic parameters. This book can serve as an excellent guide for researchers, graduate students, and lecturers interested in the development of neural networks and their training.