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Metaheuristic Procedures for Training Neural Networks (Operations Research/Computer Science Interfaces Series)

  • 250 Pages
  • 3.77 MB
  • 8948 Downloads
  • English

Springer
ContributionsEnrique Alba (Editor), Rafael Marti (Editor)
The Physical Object
ID Numbers
Open LibraryOL7445503M
ISBN 100387334157
ISBN 139780387334158

‎Metaheuristic Procedures For Training Neural Networks provides successful implementations of metaheuristic methods for neural network training.

Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on. Metaheuristic Procedures for Training Neural Networks (Operations Research/Computer Science Interfaces Series (35)) th Edition by Enrique Alba (Editor).

This book provides successful implementations of metaheuristic methods for neural network training.

Details Metaheuristic Procedures for Training Neural Networks (Operations Research/Computer Science Interfaces Series) FB2

It is the first book to achieve this objective. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training Author: Enrique Alba.

Metaheuristic Procedures For Training Neural Networks provides successful implementations of metaheuristic methods for neural network training. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own.

"Metaheuristic Procedures For Training Neural Networks provides successful implementations of metaheuristic methods for neural network training. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own.

Metaheuristic methods for training neural networks are based on local search, population methods, and others such as cooperative coevolutionary models [3]. An excellent work where the authors show.

Metaheuristic Optimization Algorithms for Training Artificial Neural Networks Ahmad AL Kawam, Nashat Mansour. Computer Science Department, Lebanese American University. Beirut, Lebanon. Emails: [email protected], [email protected] Abstract — Training neural networks is a complex task that is important for supervised learning.

A few. Neural network training using firefly algorithm. Glob J Adv Eng Sci 1(1): 7 – [Google Scholar] Mavrovouniotis M, Yang S. Training neural networks with ant colony optimization algorithms for pattern classification. Soft Comput. 19(6): – doi: /s, [Web of Science ®], [Google Scholar].

In this paper an application of a new metaheuristic called population learning algorithm (PLA) to ANN is investigated. The paper proposes several implementations of the PLA to training feed-forward artificial neural networks. 1. Introduction. Deep learning (DL) is mainly motivated by the research of artificial intelligent, in which the general goal is to imitate the ability of human brain to observe, analyze, learn, and make a decision, especially for complex problem [].This technique is in the intersection amongst the research area of signal processing, neural network, graphical modeling, optimization, and pattern.

Alba, E. and Chicano, F. () 'Genetic algorithms', in Alba, E. and Martí, R. (Eds.): Metaheuristic Procedures for Training Neural Networks, pp. Springer. Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research Varun Kumar Ojha1, Ajith Abraham2, and V aclav Sn a sel1 1Dept.

of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic 2Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA Abstract Over the past two decades, the feedforward neural network (FNN). Artificial Neural Network and Metaheuristic Strategies: Emerging Tools for Metal Cutting Process Optimization: /ch Application of optimization tools and techniques is necessary and an essential requirement for any metal cutting-based manufacturing unit to respond.

Metaheuristic procedures aimed at finding the global optimum (usually with a certain prob- Optimization Algorithm on training feed-forward neural networks to classify different data sets. Deep learning (DL) is a type of machine learning that mimics the thinking patterns of a human brain to learn the new abstract features automatically by deep and hierarchical layers.

DL is implemented by deep neural network (DNN) which has multi-hidden layers. DNN is developed from traditional artificial neural network (ANN).

Description Metaheuristic Procedures for Training Neural Networks (Operations Research/Computer Science Interfaces Series) PDF

However, in the training process of DL, it has certain. Swarm-Based Nature-Inspired Metaheuristics for Neural Network Optimization: /ch Nature-inspired algorithms have been productively applied to train neural network architectures. There exist other mechanisms like gradient descent, second.

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of syn. METAHEURISTIC PROCEDURES FOR TRAINING NEURAL NETWORKS provides successful implementations of metaheuristic methods for neural network training.

Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training. In this research, a new metaheuristic optimization algorithm, inspired by biological nervous systems and artificial neural networks (ANNs) is proposed for solving complex optimization problems.

The proposed method, named as neural network algorithm (NNA), is developed based on the unique structure of ANNs.

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM).

This paper is organized as follows: Section 1 is an introduction, Section 2 explains the used metaheuristic algorithms, Section 3 describes the convolution neural networks, Section 4 gives a description of the proposed methods, Section 5 presents the result of simulation, and Section 6.

Special thanks and credits. Base for pseudo code and many ideas are directly derived from my course book, "Artificial Intelligence: A Modern Approach (Third edition) by Stuart Russell and Peter Norvig".That book is simply amazing.

A huge part of neural networks pseudo code, Python instructions and suggestions are from Stephen and his video lessons.

Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide.

Discover Book Depository's huge selection of Enrique Alba books online. Free delivery worldwide on over 20 million titles. Metaheuristic Procedures for Training Neural Networks. Enrique Alba. 17 May Hardback. US$ US$ Save US$ Add to basket. Metaheuristic Procedures for Training Neural Networks.

Enrique Alba. 19 Nov. A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry.

However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated.

New Delhi: New Age Publishers, p. This book attempts to provide the reader with basic concepts and engineering applications of Fuzzy Logic and Neural Networks. Some of the material in this book contains timely material and thus may heavily change throughout the ages.

Download Metaheuristic Procedures for Training Neural Networks (Operations Research/Computer Science Interfaces Series) FB2

The choice of. [PDF] Global Production Networks: Operations Design and Management, Second Edition Full Colection. The network training (i.e. the change on its weights according to the way in which an input value pertaining to the training data is mapped by a network through based on an appropriate output value) begins with randomly selected weights and applies a learning algorithm, namely, the BP of errors (Mao et al., ).

In order to enhance this. Book Part: Publisher: Springer: Abstract: Simulated Annealing is a meta-heuristic that performs a randomized local search to reach near-optimal solutions of combinatorial as well as continuous optimization problems, In this chapter we show how it can be used to train artificial neural networks.

Neural networks—an overview The term "Neural networks" is a very evocative one. It suggests machines that are something like brains and is potentially laden with the science fiction connotations of the Frankenstein mythos.

One of the main tasks of this book is to demystify neural networks and show how, while they indeed have something to do. S. Li, D. C. Wunsch, E. O'Hair, and M. G. Giesselmann, “Wind turbine power estimation by neural networks with Kalman filter training on a SIMD parallel machine,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '99), pp.

–, Washington, DC, USA, July View at: Publisher Site | Google Scholar.books neural network training using Neural Network Training Using Genetic A small group of examples with size optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum.

Hence, they are.Metaheuristic methods for training neural networks are based on local search, population methods, and others such as cooperative coevolutionary models. An excellent work where the authors show an extensive literature review of evolutionary algorithms that are used to evolve ANN is [ 2 ].