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Author : Larry A. Taylor
Publisher :
ISBN 13 :
Total Pages : 166 pages
Book Rating : 4.:/5 (188 download)
Download or read book Competitive Learning Models for a Neural Network written by Larry A. Taylor and published by . This book was released on 1988 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Seyed Jalal Kia
Publisher :
ISBN 13 :
Total Pages : 231 pages
Book Rating : 4.:/5 (228 download)
Download or read book New Neural Network Models Based on Unsupervised Competitive Learning written by Seyed Jalal Kia and published by . This book was released on 1993 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Fouad Sabry
Publisher : One Billion Knowledgeable
ISBN 13 :
Total Pages : 132 pages
Book Rating : 4.:/5 (661 download)
Download or read book Competitive Learning written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-21 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Competitive Learning In artificial neural networks, competitive learning is a type of unsupervised learning in which nodes fight for the right to respond to a subset of the input data. This type of learning is known as "competitive learning." Competitive learning is a form of learning that is similar to Hebbian learning. It operates by raising the level of specialization at each node in the network. It works quite well for discovering clusters hidden within data. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Competitive Learning Chapter 2: Self-organizing map Chapter 3: Perceptron Chapter 4: Unsupervised Learning Chapter 5: Hebbian Theory Chapter 6: Backpropagation Chapter 7: Multilayer Perceptron Chapter 8: Learning Rule Chapter 9: Feature Learning Chapter 10: Types of artificial neural networks (II) Answering the public top questions about competitive learning. (III) Real world examples for the usage of competitive learning in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of competitive learning. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Author : Nāṣir Aḥmad
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (124 download)
Download or read book Competitive Learning in Rate Coded and Spiking Neural Networks Models with Applications to Vision and Audition written by Nāṣir Aḥmad and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Dr.K.Saravanan
Publisher : Leilani Katie Publication
ISBN 13 : 8196994451
Total Pages : 132 pages
Book Rating : 4.1/5 (969 download)
Download or read book Neural Networks and Deep Learning written by Dr.K.Saravanan and published by Leilani Katie Publication. This book was released on 2024-02-05 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr.K.Saravanan, Assistant Professor, Department of Mathematics, Shree Amirtha College of Education, Namakkal, Tamil Nadu, India. Dr. O. Nethaji, Assistant Professor, PG and Research Department of Mathematics, Kamaraj College, Manonmaniam Sundaranar University, Thoothukudi, Tamilnadu, India. Mrs.V.Suganthi, Assistant Professor, Department of Computer Science, C.T.T.E College for Women, University of Madras, Chennai, Tamil Nadu, India. Dr.Sangeetha Rajendran, Assistant Professor, Department of Computer Science, Mangayarkarasi College of Arts and Science for Women, Madurai, Tamil Nadu, India. Dr.P.Murugabharathi, Guest Faculty, Mother Teresa Women's University Research and Extension Centre, Chennai, Tamil Nadu, India.
Author : Wulfram Gerstner
Publisher : Cambridge University Press
ISBN 13 : 1107060834
Total Pages : 591 pages
Book Rating : 4.1/5 (7 download)
Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Author : Wenjun Zhang
Publisher : World Scientific
ISBN 13 : 9814282634
Total Pages : 310 pages
Book Rating : 4.8/5 (142 download)
Download or read book Computational Ecology written by Wenjun Zhang and published by World Scientific. This book was released on 2010 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ch. 1. Introduction. 1. Computational ecology. 2. Artificial neural networks and ecological applications -- pt. I. Artificial neural networks : principles, theories and algorithms. ch. 2. Feedforward neural networks. 1. Linear separability and perceptron. 2. Some analogies of multilayer feedforward networks. 3. Functionability of multilayer feedforward networks. ch. 3. Linear neural networks. 1. Linear neural networks. 2. LMS rule. ch. 4. Radial basis function neural networks. 1. Theory of RBF neural network. 2. Regularized RBF neural network. 3. RBF neural network learning. 4. Probabilistic neural network. 5. Generalized regression neural network. 6. Functional link neural network. 7. Wavelet neural network. ch. 5. BP neural network. 1. BP algorithm. 2. BP theorem. 3. BP training. 4. Limitations and improvements of BP algorithm. ch. 6. Self-organizing neural networks. 1. Self-organizing feature map neural network. 2. Self-organizing competitive learning neural network. 3. Hamming neural network. 4. WTA neural network. 5. LVQ neural network. 6. Adaptive resonance theory. ch. 7. Feedback neural networks. 1. Elman neural network. 2. Hopfield neural networks. 3. Simulated annealing. 4. Boltzmann machine. ch. 8. Design and customization of artificial neural networks. 1. Mixture of experts. 2. Hierarchical mixture of experts. 3. Neural network controller. 4. Customization of neural networks. ch. 9. Learning theory, architecture choice and interpretability of neural networks. 1. Learning theory. 2. Architecture choice. 3. Interpretability of neural networks. ch. 10. Mathematical foundations of artificial neural networks. 1. Bayesian methods. 2. Randomization, bootstrap and Monte Carlo techniques. 3. Stochastic process and stochastic differential equation. 4. Interpolation. 5. Function approximation. 6. Optimization methods. 7. Manifold and differential geometry. 8. Functional analysis. 9. Algebraic topology. 10. Motion stability. 11. Entropy of a system. 12. Distance or similarity measures. ch. 11. Matlab neural network toolkit. 1. Functions of perceptron. 2. Functions of linear neural networks. 3. Functions of BP neural network. 4. Functions of self-organizing neural networks. 5. Functions of radial basis neural networks. 6. Functions of probabilistic neural network. 7. Function of generalized regression neural network. 8. Functions of Hopfield neural network. 9. Function of Elman neural network -- pt. II. Applications of artificial neural networks in ecology. ch. 12. Dynamic modeling of survival process. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 13. Simulation of plant growth process. 1. Model description. 2. Data source. 3. Results. 4. Discussion. ch. 14. Simulation of food intake dynamics. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 15. Species richness estimation and sampling data documentation. 1. Estimation of plant species richness on grassland. 2. Documentation of sampling data of invertebrates. ch. 16. Modeling arthropod abundance from plant composition of grassland community. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 17. Pattern recognition and classification of ecosystems and functional groups. 1. Model description. 2. Data source. 3. Results. 4. Discussion. ch. 18. Modeling spatial distribution of arthropods. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 19. Risk assessment of species invasion and establishment. 1. Invasion risk assessment based on species assemblages. 2. Determination of abiotic factors influencing species invasion. ch. 20. Prediction of surface ozone. 1. BP prediction of daily total ozone. 2. MLP Prediction of hourly ozone levels. ch. 21. Modeling dispersion and distribution of oxide and nitrate pollutants. 1. Modeling nitrogen dioxide dispersion. 2. Simulation of nitrate distribution in ground water. ch. 22. Modeling terrestrial biomass. 1. Estimation of aboveground grassland biomass. 2. Estimation of trout biomass
Author : John A. Hertz
Publisher : CRC Press
ISBN 13 : 0429979290
Total Pages : 289 pages
Book Rating : 4.4/5 (299 download)
Download or read book Introduction To The Theory Of Neural Computation written by John A. Hertz and published by CRC Press. This book was released on 2018-03-08 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
Author : Ke-Lin Du
Publisher : Springer Science & Business Media
ISBN 13 : 1846283035
Total Pages : 610 pages
Book Rating : 4.8/5 (462 download)
Download or read book Neural Networks in a Softcomputing Framework written by Ke-Lin Du and published by Springer Science & Business Media. This book was released on 2006-08-02 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise but comprehensive textbook reviews the most popular neural-network methods and their associated techniques. Each chapter provides state-of-the-art descriptions of important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms – powerful tools for neural-network learning – are introduced. The systematic survey of neural-network models and exhaustive references list will point readers toward topics for future research. The algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.
Author : Matthew Zeidenberg
Publisher : Ellis Horwood
ISBN 13 :
Total Pages : 282 pages
Book Rating : 4.3/5 (91 download)
Download or read book Neural Network Models in Artificial Intelligence written by Matthew Zeidenberg and published by Ellis Horwood. This book was released on 1990 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to provide a concise introduction to recent, representative work in the field of neural networks. Each topic provides an overview of work in one particular area and proceeds towards a review of current research and development in that area.
Author : Huajin Tang
Publisher : Springer Science & Business Media
ISBN 13 : 3540692258
Total Pages : 310 pages
Book Rating : 4.5/5 (46 download)
Download or read book Neural Networks: Computational Models and Applications written by Huajin Tang and published by Springer Science & Business Media. This book was released on 2007-03-12 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Stefan Wermter
Publisher : Springer
ISBN 13 : 3319111795
Total Pages : 874 pages
Book Rating : 4.3/5 (191 download)
Download or read book Artificial Neural Networks and Machine Learning -- ICANN 2014 written by Stefan Wermter and published by Springer. This book was released on 2014-08-18 with total page 874 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.
Author : Teuvo Kohonen
Publisher : Springer
ISBN 13 : 3662007843
Total Pages : 325 pages
Book Rating : 4.6/5 (62 download)
Download or read book Self-Organization and Associative Memory written by Teuvo Kohonen and published by Springer. This book was released on 2012-12-06 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two significant things have happened since the writing of the first edition in 1983. One of them is recent arousal of strong interest in general aspects of "neural computing", or "neural networks", as the previous neural models are nowadays called. The incentive, of course, has been to develop new com puters. Especially it may have been felt that the so-called fifth-generation computers, based on conventional logic programming, do not yet contain in formation processing principles of the same type as those encountered in the brain. All new ideas for the "neural computers" are, of course, welcome. On the other hand, it is not very easy to see what kind of restrictions there exist to their implementation. In order to approach this problem systematically, cer tain lines of thought, disciplines, and criteria should be followed. It is the pur pose of the added Chapter 9 to reflect upon such problems from a general point of view. Another important thing is a boom of new hardware technologies for dis tributed associative memories, especially high-density semiconductor circuits, and optical materials and components. The era is very close when the parallel processors can be made all-optical. Several working associative memory archi tectures, based solely on optical technologies, have been constructed in recent years. For this reason it was felt necessary to include a separate chapter (Chap. 10) which deals with the optical associative memories. Part of its con tents is taken over from the first edition.
Author : Ashok K. Agrawal
Publisher :
ISBN 13 :
Total Pages : 174 pages
Book Rating : 4.:/5 (212 download)
Download or read book Co-lateral Competative Learning written by Ashok K. Agrawal and published by . This book was released on 1989 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : K. Taylor
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781543144567
Total Pages : 334 pages
Book Rating : 4.1/5 (445 download)
Download or read book Deep Learning Using MATLAB. Neural Network Applications written by K. Taylor and published by Createspace Independent Publishing Platform. This book was released on 2017-02-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Deep learning is part of a broader family of machine learning methods based on learning representations of data. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications This book develops deep learning, including convolutional neural networks and autoencoders and other types of advanced neural networks
Author : Deepak Nulu
Publisher :
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.:/5 (344 download)
Download or read book Connectionist Neuron and Network Models for Unsupervised Competitive Learning written by Deepak Nulu and published by . This book was released on 1995 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Simon S. Haykin
Publisher : Macmillan College
ISBN 13 :
Total Pages : 728 pages
Book Rating : 4.4/5 (91 download)
Download or read book Neural Networks written by Simon S. Haykin and published by Macmillan College. This book was released on 1994 with total page 728 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning process - Correlation matrix memory - The perceptron - Least-mean-square algorithm - Multilayer perceptrons - Radial-basic function networks - Recurrent networks rooted in statistical physics - Self-organizing systems I : hebbian learning - Self-organizing systems II : competitive learning - Self-organizing systems III : information-theoretic models - Modular networks - Temporal processing - Neurodynamics - VLSI implementations of neural networks.
