Exploring Neural Networks with C#: 1st Edition (Paperback) book cover

Exploring Neural Networks with C#

1st Edition

By Ryszard Tadeusiewicz, Rituparna Chaki, Nabendu Chaki

CRC Press

298 pages | 342 B/W Illus.

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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—while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand.

Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks.

Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&lang=en


This book offers a real-life experimentation environment to readers. Moreover, it permits direct and personal exploration of neural learning and modeling. The companion software to this book is a collection of online programs that facilitate such exploratory methods and systematic self-discovery of neural networks. The programs are available in two forms—as executable applications ready for immediate use as described in the book or as source codes in C#. … As past president of IEEE’s Computational Intelligence Society with over 6,000 members and the editor-in-chief of IEEE Transactions on Neural Networks, I am very interested in the success of neural network technology. I, therefore, highly recommend this book to all who want to learn neurocomputing techniques for their unique and practical educational value.

—Dr. Jacek M. Zurada, Department of Electrical and Computer Engineering, University of Louisville, Kentucky

Table of Contents

Introduction to Natural and Artificial Neural Networks

Why Learn about Neural Networks?

From Brain Research to Artificial Neural Networks

Construction of First Neural Networks

Layered Construction of Neural Network

From Biological Brain to First Artificial Neural Network

Current Brain Research Methods

Using Neural Networks to Study the Human Mind

Simplification of Neural Networks: Comparison with Biological Networks

Main Advantages of Neural Networks

Neural Networks as Replacements for Traditional Computers

Working with Neural Networks


Neural Net Structure

Building Neural Nets

Constructing Artificial Neurons

Attempts to Model Biological Neurons

How Artificial Neural Networks Work

Impact of Neural Network Structure on Capabilities

Choosing Neural Network Structures Wisely

"Feeding" Neural Networks: Input Layers

Nature of Data: The Home of the Cow

Interpreting Answers Generated by Networks: Output Layers

Preferred Result: Number or Decision?

Network Choices: One Network with Multiple Outputs versus Multiple Networks with Single Outputs

Hidden Layers

Determining Numbers of Neurons


Questions and Self-Study Tasks

Teaching Networks

Network Tutoring


Methods of Gathering Information

Organizing Network Learning

Learning Failures

Use of Momentum

Duration of Learning Process

Teaching Hidden Layers

Learning without Teachers

Cautions Surrounding Self-Learning

Questions and Self-Study Tasks

Functioning of Simplest Networks

From Theory to Practice: Using Neural Networks

Capacity of Single Neuron

Experimental Observations

Managing More Inputs

Network Functioning

Construction of Simple Linear Neural Network

Use of Network

Rivalry in Neural Networks

Additional Applications

Questions and Self-Study Tasks

Teaching Simple Linear One-Layer Neural Networks

Building Teaching File

Teaching One Neuron

"Inborn" Abilities of Neurons


Teaching Simple Networks

Potential Uses for Simple Neural Networks

Teaching Networks to Filter Signals

Questions and Self-Study Tasks

Nonlinear Networks

Advantages of Nonlinearity

Functioning of Nonlinear Neurons

Teaching Nonlinear Networks

Demonstrating Actions of Nonlinear Neurons

Capabilities of Multilayer Networks of Nonlinear Neurons

Nonlinear Neuron Learning Sequence

Experimentation during Learning Phase

Questions and Self-Study Tasks



Changing Thresholds of Nonlinear Characteristics

Shapes of Nonlinear Characteristics

Functioning of Multilayer Network Constructed of Nonlinear Elements

Teaching Multilayer Networks

Observations during Teaching

Reviewing Teaching Results

Questions and Self-Study Tasks

Forms of Neural Network Learning

Using Multilayer Neural Networks for Recognition

Implementing a Simple Neural Network for Recognition

Selecting Network Structure for Experiments

Preparing Recognition Tasks

Observation of Learning

Additional Observations

Questions and Self-Study Tasks

Self-Learning Neural Networks

Basic Concepts

Observation of Learning Processes

Evaluating Progress of Self-Teaching

Neuron Responses to Self-Teaching

Imagination and Improvisation

Remembering and Forgetting

Self-Learning Triggers

Benefits from Competition

Results of Self-Learning with Competition

Questions and Self-Study Tasks

Self-Organizing Neural Networks

Structure of Neural Network to Create Mappings Resulting from Self-Organizing

Uses of Self-Organization

Implementing Neighborhood in Networks

Neighbor Neurons

Uses of Kohonen Networks

Kohonen Network Handling of Difficult Data

Networks with Excessively Wide Ranges of Initial Weights

Changing Self-Organization via Self-Learning

Practical Uses of Kohonen Networks

Tool for Transformation of Input Space Dimensions

Questions and Self-Study Tasks

Recurrent Networks

Description of Recurrent Neural Network

Features of Networks with Feedback

Benefits of Associative Memory

Construction of Hopfield Network

Functioning of Neural Network as Associative Memory

Program for Examining Hopfield Network Operations

Interesting Examples

Automatic Pattern Generation for Hopfield Network

Studies of Associative Memory

Other Observations of Associative Memory

Questions and Self-Study Tasks


Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Machine Theory
COMPUTERS / Programming Languages / General
COMPUTERS / Software Development & Engineering / Systems Analysis & Design