Neural We

Author : Masumi Ishikawa,Kenji Doya,Hiroyuki Miyamoto,Takeshi Yamakawa
Publisher : Springer Science & Business Media
Page : 1165 pages
File Size : 54,8 Mb
Release : 2008-06-16
Category : Computers
ISBN : 9783540691549

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Neural Information Processing by Masumi Ishikawa,Kenji Doya,Hiroyuki Miyamoto,Takeshi Yamakawa Pdf

The two volume set LNCS 4984 and LNCS 4985 constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Neural Information Processing, ICONIP 2007, held in Kitakyushu, Japan, in November 2007, jointly with BRAINIT 2007, the 4th International Conference on Brain-Inspired Information Technology. The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.

Author : Rodrigo Quian Quiroga,Stefano Panzeri
Publisher : CRC Press
Page : 625 pages
File Size : 48,7 Mb
Release : 2013-05-06
Category : Medical
ISBN : 9781439853313

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Principles of Neural Coding by Rodrigo Quian Quiroga,Stefano Panzeri Pdf

Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this b

Author : Shuai Li,Yinyan Zhang
Publisher : Springer
Page : 74 pages
File Size : 45,8 Mb
Release : 2017-10-29
Category : Technology & Engineering
ISBN : 9789811070372

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Neural Networks for Cooperative Control of Multiple Robot Arms by Shuai Li,Yinyan Zhang Pdf

This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.

Author : Ryszard Tadeusiewicz,Rituparna Chaki,Nabendu Chaki
Publisher : CRC Press
Page : 302 pages
File Size : 42,9 Mb
Release : 2017-07-27
Category : Computers
ISBN : 9781498760379

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Exploring Neural Networks with C# by Ryszard Tadeusiewicz,Rituparna Chaki,Nabendu Chaki Pdf

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical.Exploring Neural Networks with C# presents the important properties of neural networks

Author : James J. Buckley,Thomas Feuring
Publisher : Physica
Page : 161 pages
File Size : 50,6 Mb
Release : 2013-06-05
Category : Computers
ISBN : 9783790818819

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Fuzzy and Neural: Interactions and Applications by James J. Buckley,Thomas Feuring Pdf

The primary purpose of this book is to present information about selected topics on the interactions and applications of fuzzy + neural. Most of the discussion centers around our own research in these areas. Fuzzy + neural can mean many things: (1) approximations between fuzzy systems and neu ral nets (Chapter 4); (2) building hybrid neural nets to equal fuzzy systems (Chapter 5); (3) using neura.l nets to solve fuzzy problems (Chapter 6); (4) approximations between fuzzy neural nets and other fuzzy systems (Chap ter 8); (5) constructing hybrid fuzzy neural nets for certain fuzzy systems (Chapters 9, 10); or (6) computing with words (Chapter 11). This book is not intend to be used primarily as a text book for a course in fuzzy + neural because we have not included problems at the end of each chapter, we have omitted most proofs (given in the references), and we have given very few references. We wanted to keep the mathematical prerequisites to a minimum so all longer, involved, proofs were omitted. Elementary dif ferential calculus is the only prerequisite needed since we do mention partial derivatives once or twice.

Author : Igor Aizenberg
Publisher : Springer Science & Business Media
Page : 273 pages
File Size : 41,9 Mb
Release : 2011-06-24
Category : Computers
ISBN : 9783642203527

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Complex-Valued Neural Networks with Multi-Valued Neurons by Igor Aizenberg Pdf

Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.

Author : Raul Rojas
Publisher : Springer Science & Business Media
Page : 511 pages
File Size : 50,6 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9783642610684

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Neural Networks by Raul Rojas Pdf

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

Author : Akira Hirose
Publisher : Springer
Page : 176 pages
File Size : 50,8 Mb
Release : 2007-01-11
Category : Computers
ISBN : 9783540334576

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Complex-Valued Neural Networks by Akira Hirose Pdf

This monograph instructs graduate- and undergraduate-level students in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering on the concepts of complex-valued neural networks. Emphasizing basic concepts and ways of thinking about neural networks, the author focuses on neural networks that deal with complex numbers; the practical advantages of complex-valued neural networks, and their origins; the development of principal applications? The book uses detailed examples to answer these questions and more.

Author : Robert E. Kass,Uri T. Eden,Emery N. Brown
Publisher : Springer
Page : 663 pages
File Size : 41,5 Mb
Release : 2014-07-08
Category : Medical
ISBN : 9781461496021

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Analysis of Neural Data by Robert E. Kass,Uri T. Eden,Emery N. Brown Pdf

Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

Author : Benjamín Alejandro Luna
Publisher : Benjamín Alejandro Luna
Page : 25 pages
File Size : 52,5 Mb
Release : 2020-02-10
Category : Computers
ISBN : 8210379456XXX

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Neftis Turquoise Papers (Artificial neural network) by Benjamín Alejandro Luna Pdf

Neftis is a framework for creation, training and implementation of artificial neural networks. Currently only available for .Net Framework but intended to support other programming languages.

Author : Wlodzislaw Duch,Erkki Oja,Slawomir Zadrozny
Publisher : Springer
Page : 1045 pages
File Size : 49,6 Mb
Release : 2005-08-25
Category : Computers
ISBN : 9783540287568

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Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 by Wlodzislaw Duch,Erkki Oja,Slawomir Zadrozny Pdf

This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11–15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected – after a thorough peer-review process – about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.

Author : E R Caianiello
Publisher : World Scientific
Page : 440 pages
File Size : 51,9 Mb
Release : 1990-12-13
Category : Computers
ISBN : 9789814611350

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Parallel Architectures And Neural Networks - Third Italian Workshop by E R Caianiello Pdf

Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Page : 801 pages
File Size : 52,6 Mb
Release : 2016-11-10
Category : Computers
ISBN : 9780262337373

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Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Author : Věra Kůrková,Yannis Manolopoulos,Barbara Hammer,Lazaros Iliadis,Ilias Maglogiannis
Publisher : Springer
Page : 632 pages
File Size : 47,8 Mb
Release : 2018-09-25
Category : Computers
ISBN : 9783030014216

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Artificial Neural Networks and Machine Learning – ICANN 2018 by Věra Kůrková,Yannis Manolopoulos,Barbara Hammer,Lazaros Iliadis,Ilias Maglogiannis Pdf

This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The 139 full and 28 short papers as well as 41 full poster papers and 41 short poster papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Author : Terrence L. Fine
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 49,7 Mb
Release : 2006-04-06
Category : Computers
ISBN : 9780387226491

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Feedforward Neural Network Methodology by Terrence L. Fine Pdf

This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.