Neural Networks: Computational Models And Applications
Author | : Tang |
Publisher | : |
Total Pages | : 322 |
Release | : 2009-10-01 |
Genre | : |
ISBN | : 9788184894363 |
Download Neural Networks: Computational Models And Applications Book in PDF, Epub and Kindle
Download and Read Neural Networks Computational Models And Applications full books in PDF, ePUB, and Kindle. Read online free Neural Networks Computational Models And Applications ebook anywhere anytime directly on your device. We cannot guarantee that every ebooks is available!
Author | : Tang |
Publisher | : |
Total Pages | : 322 |
Release | : 2009-10-01 |
Genre | : |
ISBN | : 9788184894363 |
Author | : Huajin Tang |
Publisher | : Springer Science & Business Media |
Total Pages | : 310 |
Release | : 2007-03-12 |
Genre | : Computers |
ISBN | : 3540692258 |
Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.
Author | : Huajin Tang |
Publisher | : Springer |
Total Pages | : 310 |
Release | : 2007-03-09 |
Genre | : Computers |
ISBN | : 3540692266 |
Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.
Author | : Subana Shanmuganathan |
Publisher | : Springer |
Total Pages | : 468 |
Release | : 2016-02-03 |
Genre | : Technology & Engineering |
ISBN | : 3319284959 |
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.
Author | : Zhang, Ming |
Publisher | : IGI Global |
Total Pages | : 660 |
Release | : 2010-02-28 |
Genre | : Computers |
ISBN | : 1615207120 |
"This book introduces and explains Higher Order Neural Networks (HONNs) to people working in the fields of computer science and computer engineering, and how to use HONNS in these areas"--Provided by publisher.
Author | : Erol Gelenbe |
Publisher | : |
Total Pages | : 273 |
Release | : 1991 |
Genre | : Neural networks (Computer science) |
ISBN | : 9780444893307 |
Author | : Simone Bassis |
Publisher | : Springer |
Total Pages | : 392 |
Release | : 2015-06-05 |
Genre | : Technology & Engineering |
ISBN | : 3319181645 |
This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computational topics which are grouped together into chapters devoted to the discussion of novelties and innovations related to the field of Artificial Neural Networks as well as the use of neural networks for applications, pattern recognition, signal processing, and special topics such as the detection and recognition of multimodal emotional expressions and daily cognitive functions, and bio-inspired memristor-based networks. Providing insights into the latest research interest from a pool of international experts coming from different research fields, the volume becomes valuable to all those with any interest in a holistic approach to implement believable, autonomous, adaptive and context-aware Information Communication Technologies.
Author | : E. Gelenbe |
Publisher | : Elsevier |
Total Pages | : 233 |
Release | : 2014-06-28 |
Genre | : Computers |
ISBN | : 1483297098 |
The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoretical developments. Each paper is largely self-contained and includes a complete bibliography. The methodological part of the book contains two papers on learning, one paper which presents a computational model of intracortical inhibitory effects, a paper presenting a new development of the random neural network, and two papers on associative memory models. The applications and examples portion contains papers on image compression, associative recall of simple typed images, learning applied to typed images, stereo disparity detection, and combinatorial optimisation.
Author | : Pijush Samui |
Publisher | : Academic Press |
Total Pages | : 660 |
Release | : 2017-07-18 |
Genre | : Technology & Engineering |
ISBN | : 0128113197 |
Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods
Author | : Hava T. Siegelmann |
Publisher | : Springer Science & Business Media |
Total Pages | : 193 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 146120707X |
The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.