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Author : Claudio Turchetti
Publisher : IOS Press
ISBN 13 : 9784274906268
Total Pages : 202 pages
Book Rating : 4.9/5 (62 download)
Download or read book Stochastic Models of Neural Networks written by Claudio Turchetti and published by IOS Press. This book was released on 2004 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Gerasimos G. Rigatos
Publisher : Springer
ISBN 13 : 3662437643
Total Pages : 296 pages
Book Rating : 4.6/5 (624 download)
Download or read book Advanced Models of Neural Networks written by Gerasimos G. Rigatos and published by Springer. This book was released on 2014-08-27 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Rob J Hyndman
Publisher : OTexts
ISBN 13 : 0987507117
Total Pages : 380 pages
Book Rating : 4.9/5 (875 download)
Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author : P. S. Neelakanta
Publisher : CRC Press
ISBN 13 : 1351428969
Total Pages : 259 pages
Book Rating : 4.3/5 (514 download)
Download or read book Neural Network Modeling written by P. S. Neelakanta and published by CRC Press. This book was released on 2018-02-06 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Moritz Helias
Publisher : Springer Nature
ISBN 13 : 303046444X
Total Pages : 203 pages
Book Rating : 4.0/5 (34 download)
Download or read book Statistical Field Theory for Neural Networks written by Moritz Helias and published by Springer Nature. This book was released on 2020-08-20 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.
Author : Priscilla E. Greenwood
Publisher : Springer
ISBN 13 : 3319269119
Total Pages : 75 pages
Book Rating : 4.3/5 (192 download)
Download or read book Stochastic Neuron Models written by Priscilla E. Greenwood and published by Springer. This book was released on 2016-02-02 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain Research Centre at the University of British Columbia.
Author : Subana Shanmuganathan
Publisher : Springer
ISBN 13 : 3319284959
Total Pages : 472 pages
Book Rating : 4.3/5 (192 download)
Download or read book Artificial Neural Network Modelling written by Subana Shanmuganathan and published by Springer. This book was released on 2016-02-03 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Berndt Müller
Publisher : Springer Science & Business Media
ISBN 13 : 3642577601
Total Pages : 340 pages
Book Rating : 4.6/5 (425 download)
Download or read book Neural Networks written by Berndt Müller and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.
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 : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 319 pages
Book Rating : 4./5 ( download)
Download or read book Probability for Machine Learning written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-09-24 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.
Author : Quan-Lin Li
Publisher : Springer Nature
ISBN 13 : 981150864X
Total Pages : 497 pages
Book Rating : 4.8/5 (115 download)
Download or read book Stochastic Models in Reliability, Network Security and System Safety written by Quan-Lin Li and published by Springer Nature. This book was released on 2019-10-21 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is dedicated to Jinhua Cao on the occasion of his 80th birthday. Jinhua Cao is one of the most famous reliability theorists. His main contributions include: published over 100 influential scientific papers; published an interesting reliability book in Chinese in 1986, which has greatly influenced the reliability of education, academic research and engineering applications in China; initiated and organized Reliability Professional Society of China (the first part of Operations Research Society of China) since 1981. The high admiration that Professor Cao enjoys in the reliability community all over the world was witnessed by the enthusiastic response of each contributor in this book. The contributors are leading researchers with diverse research perspectives. The research areas of the book iclude a broad range of topics related to reliability models, queueing theory, manufacturing systems, supply chain finance, risk management, Markov decision processes, blockchain and so forth. The book consists of a brief Preface describing the main achievements of Professor Cao; followed by congratulations from Professors Way Kuo and Wei Wayne Li, and by Operations Research Society of China, and Reliability Professional Society of China; and further followed by 25 articles roughly grouped together. Most of the articles are written in a style understandable to a wide audience. This book is useful to anyone interested in recent developments in reliability, network security, system safety, and their stochastic modeling and analysis.
Author : Gerasimos G. Rigatos
Publisher :
ISBN 13 : 9783662437650
Total Pages : 275 pages
Book Rating : 4.4/5 (376 download)
Download or read book Advanced Models of Neural Networks written by Gerasimos G. Rigatos and published by . This book was released on 2015 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Radek Erban
Publisher : Cambridge University Press
ISBN 13 : 1108572995
Total Pages : 322 pages
Book Rating : 4.1/5 (85 download)
Download or read book Stochastic Modelling of Reaction–Diffusion Processes written by Radek Erban and published by Cambridge University Press. This book was released on 2020-01-30 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical introduction to stochastic reaction-diffusion modelling is based on courses taught at the University of Oxford. The authors discuss the essence of mathematical methods which appear (under different names) in a number of interdisciplinary scientific fields bridging mathematics and computations with biology and chemistry. The book can be used both for self-study and as a supporting text for advanced undergraduate or beginning graduate-level courses in applied mathematics. New mathematical approaches are explained using simple examples of biological models, which range in size from simulations of small biomolecules to groups of animals. The book starts with stochastic modelling of chemical reactions, introducing stochastic simulation algorithms and mathematical methods for analysis of stochastic models. Different stochastic spatio-temporal models are then studied, including models of diffusion and stochastic reaction-diffusion modelling. The methods covered include molecular dynamics, Brownian dynamics, velocity jump processes and compartment-based (lattice-based) models.
Author : Mark D. McDonnell
Publisher : Frontiers Media SA
ISBN 13 : 2889198847
Total Pages : 158 pages
Book Rating : 4.8/5 (891 download)
Download or read book Neuronal Stochastic Variability: Influences on Spiking Dynamics and Network Activity written by Mark D. McDonnell and published by Frontiers Media SA. This book was released on 2016-07-18 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic fluctuations are intrinsic to and unavoidable at every stage of neural dynamics. For example, ion channels undergo random conformational changes, neurotransmitter release at synapses is discrete and probabilistic, and neural networks are embedded in spontaneous background activity. The mathematical and computational tool sets contributing to our understanding of stochastic neural dynamics have expanded rapidly in recent years. New theories have emerged detailing the dynamics and computational power of the balanced state in recurrent networks. At the cellular level, novel stochastic extensions to the classical Hodgkin-Huxley model have enlarged our understanding of neuronal dynamics and action potential initiation. Analytical methods have been developed that allow for the calculation of the firing statistics of simplified phenomenological integrate-and-fire models, taking into account adaptation currents or temporal correlations of the noise. This Research Topic is focused on identified physiological/internal noise sources and mechanisms. By "internal", we mean variability that is generated by intrinsic biophysical processes. This includes noise at a range of scales, from ion channels to synapses to neurons to networks. The contributions in this Research Topic introduce innovative mathematical analysis and/or computational methods that relate to empirical measures of neural activity and illuminate the functional role of intrinsic noise in the brain.
Author : Hossein Bonakdari
Publisher : Elsevier
ISBN 13 : 0323972756
Total Pages : 372 pages
Book Rating : 4.3/5 (239 download)
Download or read book Stochastic Modeling written by Hossein Bonakdari and published by Elsevier. This book was released on 2022-04-13 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software allows for new avenues in time series analysis and predictive modeling which summarize more than ten years of experience in the application of stochastic models in environmental problems. The book introduces a variety of different topics in time series in the modeling and prediction of complex environmental systems. Most importantly, all codes are user-friendly and readers will be able to use them for their cases. Users who may not be familiar with MATLAB software can also refer to the appendix. This book also guides the reader step-by-step to learn developed codes for time series modeling, provides required toolboxes, explains concepts, and applies different tools for different types of environmental time series problems. Provides video tutorials on the use of codes Includes a companion site with 3,000 lines of programming, 70 principal codes and 100 pseudo codes Highlights multiple methods to Illustrate each problem
Author : Warren B. Powell
Publisher : John Wiley & Sons
ISBN 13 : 1119815037
Total Pages : 1090 pages
Book Rating : 4.1/5 (198 download)
Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Author : Lirong Cui
Publisher : CRC Press
ISBN 13 : 1000094596
Total Pages : 464 pages
Book Rating : 4.0/5 ( download)
Download or read book Stochastic Models in Reliability Engineering written by Lirong Cui and published by CRC Press. This book was released on 2020-07-29 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collective work by many leading scientists, analysts, mathematicians, and engineers who have been working at the front end of reliability science and engineering. The book covers conventional and contemporary topics in reliability science, all of which have seen extended research activities in recent years. The methods presented in this book are real-world examples that demonstrate improvements in essential reliability and availability for industrial equipment such as medical magnetic resonance imaging, power systems, traction drives for a search and rescue helicopter, and air conditioning systems. The book presents real case studies of redundant multi-state air conditioning systems for chemical laboratories and covers assessments of reliability and fault tolerance and availability calculations. Conventional and contemporary topics in reliability engineering are discussed, including degradation, networks, dynamic reliability, resilience, and multi-state systems, all of which are relatively new topics to the field. The book is aimed at engineers and scientists, as well as postgraduate students involved in reliability design, analysis, experiments, and applied probability and statistics.
