Neural Network Modeling
Download Neural Network Modeling full books in PDF, epub, and Kindle. Read online free Neural Network Modeling ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Artificial Neural Network Modelling
Author | : Subana Shanmuganathan,Sandhya Samarasinghe |
Publsiher | : Springer |
Total Pages | : 472 |
Release | : 2016-02-03 |
Genre | : Technology & Engineering |
ISBN | : 9783319284958 |
Download Artificial Neural Network Modelling Book in PDF, Epub and Kindle
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.
Neural Network Modeling
Author | : P. S. Neelakanta,Dolores DeGroff |
Publsiher | : CRC Press |
Total Pages | : 194 |
Release | : 2018-02-06 |
Genre | : Technology & Engineering |
ISBN | : 9781351428958 |
Download Neural Network Modeling Book in PDF, Epub and Kindle
Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.
Fundamentals of Neural Network Modeling
Author | : Randolph W. Parks,Daniel S. Levine,Debra L. Long |
Publsiher | : MIT Press |
Total Pages | : 450 |
Release | : 1998 |
Genre | : Cognition |
ISBN | : 0262161753 |
Download Fundamentals of Neural Network Modeling Book in PDF, Epub and Kindle
Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble
Neural Networks Computational Models and Applications
Author | : Huajin Tang,Kay Chen Tan,Zhang Yi |
Publsiher | : Springer Science & Business Media |
Total Pages | : 310 |
Release | : 2007-03-12 |
Genre | : Computers |
ISBN | : 9783540692256 |
Download Neural Networks Computational Models and Applications Book in PDF, Epub and Kindle
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.
Semi empirical Neural Network Modeling and Digital Twins Development
Author | : Dmitriy Tarkhov,Alexander Nikolayevich Vasilyev |
Publsiher | : Academic Press |
Total Pages | : 288 |
Release | : 2019-11-23 |
Genre | : Science |
ISBN | : 9780128156520 |
Download Semi empirical Neural Network Modeling and Digital Twins Development Book in PDF, Epub and Kindle
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
Artificial Higher Order Neural Networks for Modeling and Simulation
Author | : Zhang, Ming |
Publsiher | : IGI Global |
Total Pages | : 455 |
Release | : 2012-10-31 |
Genre | : Computers |
ISBN | : 9781466621763 |
Download Artificial Higher Order Neural Networks for Modeling and Simulation Book in PDF, Epub and Kindle
"This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.
Neural Network Modeling
Author | : P. S. Neelakanta,Dolores DeGroff |
Publsiher | : CRC Press |
Total Pages | : 259 |
Release | : 2018-02-06 |
Genre | : Technology & Engineering |
ISBN | : 9781351428965 |
Download Neural Network Modeling Book in PDF, Epub and Kindle
Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.
A Comprehensive Guide to Neural Network Modeling
Author | : Steffen Skaar |
Publsiher | : Nova Science Publishers |
Total Pages | : 172 |
Release | : 2020-10-26 |
Genre | : Computers |
ISBN | : 1536185426 |
Download A Comprehensive Guide to Neural Network Modeling Book in PDF, Epub and Kindle
As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing on control, monitoring and modeling of industrial food processes.The authors emphasize the main achievements of artificial neural network modeling in recent years in the field of quantitative structure-activity relationships and quantitative structure-retention relationships.In the closing study, artificial intelligence techniques are applied to river water quality data and artificial intelligence models are developed in an effort to contribute to the reduction of the cost of future on-line measurement stations.