# Search Results for "introduction-to-neural-networks-for-c-2nd-edition"

## Introduction to Neural Networks with Java

**Author**: Jeff Heaton**Publisher:**Heaton Research, Inc.**ISBN:**1604390085**Category:**Computers**Page:**440**View:**8224

Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward, Hopfield, and Self Organizing Map networks are discussed. Training techniques such as Backpropagation, Genetic Algorithms and Simulated Annealing are also introduced. Practical examples are given for each neural network. Examples include the Traveling Salesman problem, handwriting recognition, financial prediction, game strategy, learning mathematical functions and special application to Internet bots. All Java source code can be downloaded online.

## An Introduction to the Mathematics of Neurons

*Modeling in the Frequency Domain*

**Author**: Frank C. Hoppensteadt**Publisher:**Cambridge University Press**ISBN:**9780521599290**Category:**Mathematics**Page:**211**View:**9317

This book describes signal processing aspects of neural networks, how we receive and assess information. Beginning with a presentation of the necessary background material in electronic circuits, mathematical modeling and analysis, signal processing, and neurosciences, it proceeds to applications. These applications include small networks of neurons, such as those used in control of warm-up and flight in moths and control of respiration during exercise in humans. Next, Hoppensteadt develops a theory of mnemonic surfaces and presents material on pattern formation and cellular automata. Finally, the text addresses the large networks, such as the thalamus-reticular complex circuit, that may be involved in focusing attention, and the development of connections in the visual cortex. This book will serve as an excellent text for advanced undergraduates and graduates in the physical sciences, mathematics, engineering, medicine and life sciences.

## Introduction to Neural Networks

**Author**: Phil Picton**Publisher:**N.A**ISBN:**N.A**Category:**Neural networks (Computer science)**Page:**168**View:**7813

## An Introduction to Optimization

**Author**: Edwin K. P. Chong,Stanislaw H. Zak**Publisher:**John Wiley & Sons**ISBN:**1118515153**Category:**Mathematics**Page:**640**View:**1598

Praise for the Third Edition ". . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail." —MAA Reviews Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus. This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also offers: A new chapter on integer programming Expanded coverage of one-dimensional methods Updated and expanded sections on linear matrix inequalities Numerous new exercises at the end of each chapter MATLAB exercises and drill problems to reinforce the discussed theory and algorithms Numerous diagrams and figures that complement the written presentation of key concepts MATLAB M-files for implementation of the discussed theory and algorithms (available via the book's website) Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

## PRINCIPLES OF SOFT COMPUTING, 2ND ED (With CD )

**Author**: S. N. Sivanandam,S.N. Deepa**Publisher:**John Wiley & Sons**ISBN:**N.A**Category:**Computer science**Page:**748**View:**7353

Market_Desc: · B. Tech (UG) students ofü CSEü ITü ECE· College Libraries· Research Scholars· Operational Research· Management Sector Special Features: · Detailed explanation of soft computing concepts.· Study on various artificial neural network architecture.· Description on fuzzy logic techniques.· Introduction to genetic algorithm and its types for solving optimization problems.· Numerous artificial neural network, fuzzy logic and genetic algorithm problems.· Implementation of soft computing techniques using C and C++.· Simulated solutions for soft computing concepts using MATLAB package.· Application case studies on soft computing techniques on emerging fields.· Various hybrid soft computing techniques.New in this edition· Certain topics have been added such as:ü Fundamentals of Genetic Algorithmsü Genetic Modelingü Integration of Neural Networks, Fuzzy Logic, and Genetic Algorithms· A new chapter Hybrid Soft Computing Techniques has been added bringing the advantages of combining individual techniques.· 5 Sample Question Papers have been added at the end of the book. Accompanying CD contains · Power point presentations· Source Codes for Soft Computing Techniques in C· MATLAB Source Code Programs About The Book: In this book the basic concepts of soft computing are dealt in detail with the relevant information and knowledge available for understanding the computing process. The various neural network concepts are explained with examples, highlighting the difference between various architectures. Fuzzy logic techniques have been clearly dealt with suitable examples. Genetic algorithm operators and the various classifications have been discussed in lucid manner, so that a beginner can understand the concepts with minimal effort.The book can be used as a handbook as well as a guide for students of all engineering disciplines, soft computing research scholars, management sector, operational research area, computer applications and for various professionals who work in this area.

## Intelligent Systems for Engineers and Scientists

**Author**: Adrian A. Hopgood**Publisher:**CRC Press**ISBN:**1466516178**Category:**Computers**Page:**451**View:**9595

The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance. Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/

## The Handbook of Brain Theory and Neural Networks

**Author**: Michael A. Arbib,Fletcher Jones Professor of Computer Science and Professor of Biological Sciences Biomedical Engineering Neuroscience and Psychology Michael A Arbib,Prudence H. Arbib**Publisher:**MIT Press**ISBN:**0262011972**Category:**Computers**Page:**1290**View:**2694

This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions: how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).

## Neural Nets and Chaotic Carriers

**Author**: Peter Whittle**Publisher:**World Scientific**ISBN:**1908977957**Category:**Science**Page:**244**View:**9974

Neural Nets and Chaotic Carriers develops rational principles for the design of associative memories, with a view to applying these principles to models with irregularly oscillatory operation so evident in biological neural systems, and necessitated by the meaninglessness of absolute signal levels. Design is based on the criterion that an associative memory must be able to cope with “fading data”, i.e., to form an inference from the data even as its memory of that data degrades. The resultant net shows striking biological parallels. When these principles are combined with the Freeman specification of a neural oscillator, some remarkable effects emerge. For example, the commonly-observed phenomenon of neuronal bursting appears, with gamma-range oscillation modulated by a low-frequency square-wave oscillation (the “escapement oscillation”). Bridging studies and new results of artificial and biological neural networks, the book has a strong research character. It is, on the other hand, accessible to non-specialists for its concise exposition on the basics. Contents:Opening and Themes:Introduction and AspirationsOptimal Statistical ProceduresLinear Links and Nonlinear Knots: The Basic Neural NetBifurcations and ChaosAssociative and Storage Memories:What is a Memory? The Hamming and Hopfield NetsCompound and ‘Spurious’ TracesPreserving Plasticity: A Bayesian ApproachThe Key Task: The Fixing of Fading Data. Conclusions IPerformance of the Probability-Maximising AlgorithmOther Memories — Other ConsiderationsOscillatory Operation and the Biological Model:Neuron Models and Neural MassesFreeman Oscillators — Solo and in ConcertAssociative Memories Incorporating the Freeman OscillatorOlfactory Comparisons. Conclusions IITransmission Delays Readership: Professionals and graduates in areas associated with artificial neural networks. Keywords:Neural Nets;Associative Memory;Freeman Oscillator;Neuronal Bursting;Olfactory System

## Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

**Author**: Christine Sinoquet,Raphaël Mourad**Publisher:**OUP Oxford**ISBN:**0191019208**Category:**Science**Page:**464**View:**6754

Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.

## Natural Language Processing with Java

*Techniques for building machine learning and neural network models for NLP, 2nd Edition*

**Author**: Richard M. Reese,AshishSingh Bhatia**Publisher:**Packt Publishing Ltd**ISBN:**1788993063**Category:**Computers**Page:**318**View:**4823

Explore various approaches to organize and extract useful text from unstructured data using Java Key Features Use deep learning and NLP techniques in Java to discover hidden insights in text Work with popular Java libraries such as CoreNLP, OpenNLP, and Mallet Explore machine translation, identifying parts of speech, and topic modeling Book Description Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes. You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more. By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications. What you will learn Understand basic NLP tasks and how they relate to one another Discover and use the available tokenization engines Apply search techniques to find people, as well as things, within a document Construct solutions to identify parts of speech within sentences Use parsers to extract relationships between elements of a document Identify topics in a set of documents Explore topic modeling from a document Who this book is for Natural Language Processing with Java is for you if you are a data analyst, data scientist, or machine learning engineer who wants to extract information from a language using Java. Knowledge of Java programming is needed, while a basic understanding of statistics will be useful but not mandatory.