Nostradamus Modern Methods of Prediction Modeling and Analysis of Nonlinear Systems

... Recurrent Networks with Stochastic Weight Update on Backpropagation
Through Time Juraj Koˇscák, Rudolf Jakˇsa, ... of backpropagation through time
algorithm (BPTT) done by stochastic update in the recurrent neural networks (
RCNN) ...

Nostradamus  Modern Methods of Prediction  Modeling and Analysis of Nonlinear Systems

Author: Ivan Zelinka

Publisher: Springer Science & Business Media

ISBN: 3642332277

Page: 283

View: 210

This proceeding book of Nostradamus conference (http://nostradamus-conference.org) contains accepted papers presented at this event in 2012. Nostradamus conference was held in the one of the biggest and historic city of Ostrava (the Czech Republic, http://www.ostrava.cz/en), in September 2012. Conference topics are focused on classical as well as modern methods for prediction of dynamical systems with applications in science, engineering and economy. Topics are (but not limited to): prediction by classical and novel methods, predictive control, deterministic chaos and its control, complex systems, modelling and prediction of its dynamics and much more.

Nostradamus 2013 Prediction Modeling and Analysis of Complex Systems

From the company feedback, neural networks prediction is more useful then
standardized daily temperature profiles for ... J., Jakša, R., Sinc ́ak, P.: Stochastic
weight update in the backpropagation algorithm on feed-forward neural networks
.

Nostradamus 2013  Prediction  Modeling and Analysis of Complex Systems

Author: Ivan Zelinka

Publisher: Springer Science & Business Media

ISBN: 3319005421

Page: 550

View: 841

Prediction of behavior of the dynamical systems, analysis and modeling of its structure is vitally important problem in engineering, economy and science today. Examples of such systems can be seen in the world around us and of course in almost every scientific discipline including such “exotic” domains like the earth’s atmosphere, turbulent fluids, economies (exchange rate and stock markets), population growth, physics (control of plasma), information flow in social networks and its dynamics, chemistry and complex networks. To understand such dynamics and to use it in research or industrial applications, it is important to create its models. For this purpose there is rich spectra of methods, from classical like ARMA models or Box Jenkins method to such modern ones like evolutionary computation, neural networks, fuzzy logic, fractal geometry, deterministic chaos and more. This proceeding book is a collection of the accepted papers to conference Nostradamus that has been held in Ostrava, Czech Republic. Proceeding also comprises of outstanding keynote speeches by distinguished guest speakers: Guanrong Chen (Hong Kong), Miguel A. F. Sanjuan (Spain), Gennady Leonov and Nikolay Kuznetsov (Russia), Petr Škoda (Czech Republic). The main aim of the conference is to create periodical possibility for students, academics and researchers to exchange their ideas and novel methods. This conference will establish forum for presentation and discussion of recent trends in the area of applications of various predictive methods for researchers, students and academics.

Machine Learning Optimization and Data Science

This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019.

Machine Learning  Optimization  and Data Science

Author: Giuseppe Nicosia

Publisher: Springer Nature

ISBN: 3030375994

Page: 772

View: 243

This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Advanced Models of Neural Networks

It has been shown that update of the weights based on the increase and
decrease operators satisfies two basic postulates of quantum mechanics, i.e.: (a)
existence of the stochastic weights wij in a superposition of states, (b) evolution of
the ...

Advanced Models of Neural Networks

Author: Gerasimos G. Rigatos

Publisher: Springer

ISBN: 3662437643

Page: 275

View: 621

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Neural Networks Tricks of the Trade

If the learning rate is reduced while the network is learning normally, the
reconstruction error will usually fall significiantly. This is not necessarily a good
thing. It is due, in part, to the smaller noise level in the stochastic weight updates
and it is ...

Neural Networks  Tricks of the Trade

Author: Grégoire Montavon

Publisher: Springer

ISBN: 3642352898

Page: 769

View: 970

The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

Cellular Neural Networks and Analog VLSI

However, the time scale for weight updating is generally orders of magnitude
longer than that of circuit relaxation. In most cases ... A general feature of a
learning rule which scales to large networks is that it depends only on the
instantaneous values of local ... Stochastic weight updates may partially alleviate
this problem.

Cellular Neural Networks and Analog VLSI

Author: Leon Chua

Publisher: Springer Science & Business Media

ISBN: 1475747306

Page: 103

View: 173

Cellular Neural Networks and Analog VLSI brings together in one place important contributions and up-to-date research results in this fast moving area. Cellular Neural Networks and Analog VLSI serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

Stochastic Models of Neural Networks

This book is intended to provide a treatment of the theory and applications of Stochastic Neural Networks, that is networks able to learn random processes from experience, on the basis of recent developments on this subject.

Stochastic Models of Neural Networks

Author: Claudio Turchetti

Publisher: IOS Press

ISBN: 9781586033880

Page: 173

View: 679

This book is intended to provide a treatment of the theory and applications of Stochastic Neural Networks, that is networks able to learn random processes from experience, on the basis of recent developments on this subject. The mathematical frameworks on which the theory is founded embrace the approximation of non-random functions as well as the theory of stochastic processes. The networks so defined constitute an original and very promising model of human brain neural activity consistent with the need of learning from a stochastic environment. Moreover, the problem of speech modeling, both for synthesis and recognition, is faced as concrete and significant application in the field of artificial intelligence of the theory is presented

Artificial Neural Networks

Stochastic computing leads to trivial hardware implementation and is considered
to have significant potential in neural ... The weight updates introduced using
stochastic pulses , input to the low pass filter which , for an appropriate choice of
 ...

Artificial Neural Networks

Author: IEE.

Publisher: Inst of Engineering & Technology

ISBN: 9780852963883

Page: 405

View: 942

Neural Network Models

Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way.

Neural Network Models

Author: Philippe de Wilde

Publisher: Springer Science & Business Media

ISBN: 9783540761297

Page: 174

View: 847

Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. It presents the essential principles of nonlinear dynamics as derived from neurobiology, and investigates the stability, convergence behaviour and capacity of networks.

Neural Networks and Analog Computation

The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines.

Neural Networks and Analog Computation

Author: Hava T. Siegelmann

Publisher: Springer Science & Business Media

ISBN: 146120707X

Page: 181

View: 475

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.

Neural Networks and Deep Learning

This book covers both classical and modern models in deep learning.

Neural Networks and Deep Learning

Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 3319944630

Page: 497

View: 329

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Principles of Artificial Neural Networks

Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks.

Principles of Artificial Neural Networks

Author: Daniel Graupe

Publisher: World Scientific

ISBN: 9814522740

Page: 364

View: 942

Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition OCo all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained. The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining."

Neural Networks

In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets.

Neural Networks

Author: Raul Rojas

Publisher: Springer Science & Business Media

ISBN: 3642610684

Page: 502

View: 286

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.

MATLAB Deep Learning

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks.

MATLAB Deep Learning

Author: Phil Kim

Publisher: Apress

ISBN: 1484228456

Page: 151

View: 353

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

Neural Network Time Series

The order of presentations in each epoch is usually best randomised so that the
weight updates do not fall into a set ... summarised in the following MLP network
training algorithm , where the stochastic procedure of updating the weights after ...

Neural Network Time Series

Author: E. Michael Azoff

Publisher: John Wiley & Son Limited

ISBN:

Page: 196

View: 798

Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software.

Advances in Neural Networks ISNN 2019

This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019.

Advances in Neural Networks     ISNN 2019

Author: Huchuan Lu

Publisher: Springer

ISBN: 3030227960

Page: 483

View: 781

This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019. The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal Processing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware.

Computational Ecology

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.

Computational Ecology

Author: Wenjun Zhang

Publisher: World Scientific

ISBN: 9814282634

Page: 296

View: 237

Due to the complexity and non-linearity of most ecological problems, artificial neural networks (ANNs) have attracted attention from ecologists and environmental scientists in recent years. As these networks are increasingly being used in ecology for modeling, simulation, function approximation, prediction, classification and data mining, this unique and self-contained book will be the first comprehensive treatment of this subject, by providing readers with overall and in-depth knowledge on algorithms, programs, and applications of ANNs in ecology. Moreover, a new area of ecology, i.e., computational ecology, is proposed and its scopes and objectives are defined and discussed. Computational Ecology consists of two parts: the first describes the methods and algorithms of ANNs, interpretability and mathematical generalization of neural networks, Matlab neural network toolkit, etc., while the second provides case studies of applications of ANNs in ecology, Matlab codes, and comparisons of ANNs with conventional methods.This publication will be a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of ecology, environmental sciences, and computational science.

Artificial Neural Networks and Machine Learning ICANN 2019 Deep Learning

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019.

Artificial Neural Networks and Machine Learning     ICANN 2019  Deep Learning

Author: Igor V. Tetko

Publisher: Springer Nature

ISBN: 3030304841

Page: 807

View: 302

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

On Line Learning in Neural Networks

Edited volume written by leading experts providing state-of-art survey in on-line learning and neural networks.

On Line Learning in Neural Networks

Author: David Saad

Publisher: Cambridge University Press

ISBN: 9780521117913

Page: 412

View: 406

Edited volume written by leading experts providing state-of-art survey in on-line learning and neural networks.