What Is Neural Networks?
Using the Documentation
Neural Network Applications
Applications in this Toolbox
Business Applications
Summary
Examples
Neuron Model and Network Architectures
Neuron Model
Simple Neuron
Transfer Functions
Neuron with Vector Input
Network Architectures
A Layer of Neurons
Multiple Layers of Neurons
Data Structures
Simulation with Concurrent Inputs in a Static Network
Simulation with Sequential Inputs in a Dynamic Network
Simulation with Concurrent Inputs in a Dynamic Network
Training Styles
Incremental Training (of Adaptive and Other Networks)
Batch Training
Training Tip
Summary
Equations
Perceptrons
Introduction
Important Perceptron Functions
Neuron Model
Perceptron Architecture
Creating a Perceptron (newp)
Simulation (sim)
Initialization (init)
Learning Rules
Perceptron Learning Rule (learnp)
Training (train)
Limitations and Cautions
Outliers and the Normalized Perceptron Rule
Graphical User Interface
Introduction to the GUI
Create a Perceptron Network (nntool)
Train the Perceptron
Export Perceptron Results to Workspace
Clear Network/Data Window
Importing from the Command Line
Save a Variable to a File and Load It Later
Summary
Equations
New Functions
Linear Filters
Introduction
Neuron Model
Network Architecture
Creating a Linear Neuron (newlin)
Mean Square Error
Linear System Design (newlind)
Linear Networks with Delays
Tapped Delay Line
Linear Filter
LMS Algorithm (learnwh)
Linear Classification (train)
Limitations and Cautions
Overdetermined Systems
Underdetermined Systems
Linearly Dependent Vectors
Too Large a Learning Rate
Summary
Equations
New Functions
Backpropagation
Introduction
Fundamentals
Architecture
Simulation (sim)
Training
Faster Training
Variable Learning Rate (traingda, traingdx)
Resilient Backpropagation (trainrp)
Conjugate Gradient Algorithms
Line Search Routines
Quasi-Newton Algorithms
Levenberg-Marquardt (trainlm)
Reduced Memory Levenberg-Marquardt (trainlm)
Speed and Memory Comparison
Summary
Improving Generalization
Regularization
Early Stopping
Summary and Discussion
Preprocessing and Postprocessing
Min and Max (premnmx, postmnmx, tramnmx)
Mean and Stand. Dev. (prestd, poststd, trastd)
Principal Component Analysis (prepca, trapca)
Post-Training Analysis (postreg)
Sample Training Session
Limitations and Cautions
Summary
Control Systems
Introduction
NN Predictive Control
System Identification
Predictive Control
Using the NN Predictive Controller Block
NARMA-L2 (Feedback Linearization) Control
Identification of the NARMA-L2 Model
NARMA-L2 Controller
Using the NARMA-L2 Controller Block
Model Reference Control
Using the Model Reference Controller Block
Importing and Exporting
Importing and Exporting Networks
Importing and Exporting Training Data
Summary
Radial Basis Networks
Introduction
Important Radial Basis Functions
Radial Basis Functions
Neuron Model
Network Architecture
Exact Design (newrbe)
More Efficient Design (newrb)
Demonstrations
Generalized Regression Networks
Network Architecture
Design (newgrnn)
Probabilistic Neural Networks
Network Architecture
Design (newpnn)
Summary
New Functions
Self-Organizing and Learn. Vector Quant. Nets
Introduction
Important Self-Organizing and LVQ Functions
Competitive Learning
Architecture
Creating a Competitive Neural Network (newc)
Kohonen Learning Rule (learnk)
Bias Learning Rule (learncon)
Training
Graphical Example
Self-Organizing Maps
Topologies (gridtop, hextop, randtop)
Distance Funct. (dist, linkdist, mandist, boxdist)
Architecture
Creating a Self Organizing MAP Neural Network (newsom)
Training (learnsom)
Examples
Learning Vector Quantization Networks
Architecture
Creating an LVQ Network (newlvq)
LVQ1 Learning Rule (learnlv1)
Training
Supplemental LVQ2.1 Learning Rule (learnlv2)
Summary
Self-Organizing Maps
Learning Vector Quantization Networks
New Functions
Adaptive Filters and Adaptive Training
Introduction
Important Adaptive Functions
Linear Neuron Model
Adaptive Linear Network Architecture
Single ADALINE (newlin)
Mean Square Error
LMS Algorithm (learnwh)
Adaptive Filtering (adapt)
Tapped Delay Line
Adaptive Filter
Adaptive Filter Example
Prediction Example
Noise Cancellation Example
Multiple Neuron Adaptive Filters
Summary
Equations
New Functions
Applications
Introduction
Application Scripts
Applin1: Linear Design
Problem Definition
Network Design
Network Testing
Thoughts and Conclusions
Applin2: Adaptive Prediction
Problem Definition
Network Initialization
Network Training
Network Testing
Thoughts and Conclusions
Appelm1: Amplitude Detection
Problem Definition
Network Initialization
Network Training
Network Testing
Network Generalization
Improving Performance
Appcr1: Character Recognition
Problem Statement
Neural Network
System Performance
Summary
Advanced Topics
Custom Networks
Custom Network
Network Definition
Network Behavior
Additional Toolbox Functions
Initialization Functions
Transfer Functions
Learning Functions
Custom Functions
Simulation Functions
Initialization Functions
Learning Functions
Self-Organizing Map Functions
Historical Networks
Introduction
Important Recurrent Network Functions
Elman Networks
Architecture
Creating an Elman Network (newelm)
Training an Elman Network
Hopfield Network
Fundamentals
Architecture
Design (newhop)
Summary
New Functions
Network Object Reference
Network Properties
Architecture
Subobject Structures
Functions
Parameters
Weight and Bias Values
Other
Subobject Properties
Inputs
Layers
Outputs
Targets
Biases
Input Weights
Layer Weights
Functions -- Categorical List
Analysis Functions
Distance Functions
Graphical Interface Function
Layer Initialization Functions
Learning Functions
Line Search Functions
Net Input Derivative Functions
Net Input Functions
Network Functions
Network Initialization Function
Network Use Functions
New Networks Functions
Performance Derivative Functions
Performance Functions
Plotting Functions
Pre- and Postprocessing Functions
Simulink Support Function
Topology Functions
Training Functions
Transfer Derivative Functions
Transfer Functions
Utility Functions
Vector Functions
Weight and Bias Initialization Functions
Weight Derivative Functions
Weight Functions
Transfer Function Graphs
Functions -- Alphabetical List
Glossary
Mathematical Notation
Mathematical Notation for Equations and Figures
Basic Concepts
Language
Weight Matrices
Bias Elements and Vectors
Time and Iteration
Layer Notation
Figure and Equation Examples
Mathematics and Code Equivalents
Demonstrations and Applications
Tables of Demonstrations and Applications
Chapter 2: Neuron Model and Network Architectures
Chapter 3: Perceptrons
Chapter 4: Linear Filters
Chapter 5: Backpropagation
Chapter 7: Radial Basis Networks
Chapter 8: Self-Organizing and Learn. Vector Quant. Nets
Chapter 12: Recurrent Networks
Chapter 9: Adaptive Networks
Chapter 10: Applications
Simulink
Block Set
Transfer Function Blocks
Net Input Blocks
Weight Blocks
Block Generation
Example
Exercises
Code Notes
Dimensions
Variables
Utility Function Variables
Functions
Code Efficiency
Argument Checking
Bibliography
Printable Documentation (PDF)
Product Page