C++ Neural Networks and Fuzzy Logic - Valluru B. Rao.pdf

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C++ Neural Networks and Fuzzy Logic - Table of Contents
C++ Neural Networks and Fuzzy Logic
by Valluru B. Rao
M&T Books, IDG Books Worldwide, Inc.
ISBN:
1558515526
Pub Date:
06/01/95
Preface
Dedication
Chapter 1—Introduction to Neural Networks
Neural Processing
Neural Network
Output of a Neuron
Cash Register Game
Weights
Training
Feedback
Supervised or Unsupervised Learning
Noise
Memory
Capsule of History
Neural Network Construction
Sample Applications
Qualifying for a Mortgage
Cooperation and Competition
Example—A Feed-Forward Network
Example—A Hopfield Network
Hamming Distance
Asynchronous Update
Binary and Bipolar Inputs
Bias
Another Example for the Hopfield Network
Summary
Chapter 2—C++ and Object Orientation
Introduction to C++
Encapsulation
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C++ Neural Networks and Fuzzy Logic - Table of Contents
Data Hiding
Constructors and Destructors as Special Functions of C++
Dynamic Memory Allocation
Overloading
Polymorphism and Polymorphic Functions
Overloading Operators
Inheritance
Derived Classes
Reuse of Code
C++ Compilers
Writing C++ Programs
Summary
Chapter 3—A Look at Fuzzy Logic
Crisp or Fuzzy Logic?
Fuzzy Sets
Fuzzy Set Operations
Union of Fuzzy Sets
Intersection and Complement of Two Fuzzy Sets
Applications of Fuzzy Logic
Examples of Fuzzy Logic
Commercial Applications
Fuzziness in Neural Networks
Code for the Fuzzifier
Fuzzy Control Systems
Fuzziness in Neural Networks
Neural-Trained Fuzzy Systems
Summary
Chapter 4—Constructing a Neural Network
First Example for C++ Implementation
Classes in C++ Implementation
C++ Program for a Hopfield Network
Header File for C++ Program for Hopfield Network
Notes on the Header File Hop.h
Source Code for the Hopfield Network
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C++ Neural Networks and Fuzzy Logic - Table of Contents
Comments on the C++ Program for Hopfield Network
Output from the C++ Program for Hopfield Network
Further Comments on the Program and Its Output
A New Weight Matrix to Recall More Patterns
Weight Determination
Binary to Bipolar Mapping
Pattern’s Contribution to Weight
Autoassociative Network
Orthogonal Bit Patterns
Network Nodes and Input Patterns
Second Example for C++ Implementation
C++ Implementation of Perceptron Network
Header File
Implementation of Functions
Source Code for Perceptron Network
Comments on Your C++ Program
Input/Output for percept.cpp
Network Modeling
Tic-Tac-Toe Anyone?
Stability and Plasticity
Stability for a Neural Network
Plasticity for a Neural Network
Short-Term Memory and Long-Term Memory
Summary
Chapter 5—A Survey of Neural Network Models
Neural Network Models
Layers in a Neural Network
Single-Layer Network
XOR Function and the Perceptron
Linear Separability
A Second Look at the XOR Function: Multilayer Perceptron
Example of the Cube Revisited
Strategy
Details
Performance of the Perceptron
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C++ Neural Networks and Fuzzy Logic - Table of Contents
Other Two-layer Networks
Many Layer Networks
Connections Between Layers
Instar and Outstar
Weights on Connections
Initialization of Weights
A Small Example
Initializing Weights for Autoassociative Networks
Weight Initialization for Heteroassociative Networks
On Center, Off Surround
Inputs
Outputs
The Threshold Function
The Sigmoid Function
The Step Function
The Ramp Function
Linear Function
Applications
Some Neural Network Models
Adaline and Madaline
Backpropagation
Figure for Backpropagation Network
Bidirectional Associative Memory
Temporal Associative Memory
Brain-State-in-a-Box
Counterpropagation
Neocognitron
Adaptive Resonance Theory
Summary
Chapter 6—Learning and Training
Objective of Learning
Learning and Training
Hebb’s Rule
Delta Rule
Supervised Learning
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C++ Neural Networks and Fuzzy Logic - Table of Contents
Generalized Delta Rule
Statistical Training and Simulated Annealing
Radial Basis-Function Networks
Unsupervised Networks
Self-Organization
Learning Vector Quantizer
Associative Memory Models and One-Shot Learning
Learning and Resonance
Learning and Stability
Training and Convergence
Lyapunov Function
Other Training Issues
Adaptation
Generalization Ability
Summary
Chapter 7—Backpropagation
Feedforward Backpropagation Network
Mapping
Layout
Training
Illustration: Adjustment of Weights of Connections from a Neuron in
the Hidden Layer
Illustration: Adjustment of Weights of Connections from a Neuron in
the Input Layer
Adjustments to Threshold Values or Biases
Another Example of Backpropagation Calculations
Notation and Equations
Notation
Equations
C++ Implementation of a Backpropagation Simulator
A Brief Tour of How to Use the Simulator
C++ Classes and Class Hierarchy
Summary
Chapter 8—BAM: Bidirectional Associative Memory
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