| Neural Network Toolbox |
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- Neural Networks
- Basic Chapters
- Mathematical Notation for Equations and Figures
- Basic Concepts
- Language
- Weight Matrices
- Layer Notation
- Figure and Equation Examples
- Mathematics and Code Equivalents
- Neural Network Design Book
- Acknowledgments
- Getting Started
- Basic Chapters
- Help and Installation
- What's New in Version 4.0
- Control System Applications
- Graphical User Interface
- New Training Functions
- Design of General Linear Networks
- Improved Early Stopping
- Generalization and Speed Benchmarks
- Demonstration of a Sample Training Session
- Neural Network Applications
- Applications in this Toolbox
- Business Applications
- Aerospace
- Automotive
- Banking
- Credit Card Activity Checking
- Defense
- Electronics
- Entertainment
- Financial
- Industrial
- Insurance
- Manufacturing
- Medical
- Oil and Gas
- Robotics
- Speech
- Securities
- Telecommunications
- Transportation
- Summary
- 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
- Summary
- Figures and Equations
- 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
- Figures and Equations
- New Functions
- 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
- Figures and Equations
- New Functions
- Overview
- 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
- 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
- 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
- Figures
- New Functions
- 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 and Conclusions
- Self-Organizing Maps
- Learning Vector Quantizaton Networks
- Figures
- New Functions
- Introduction
- Important Recurrent Network Functions
- Elman Networks
- Architecture
- Creating an Elman Network (newelm)
- Training an Elman Network
- Hopfield Network
- Fundamentals
- Architecture
- Design (newhop)
- Summary
- Figures
- New Functions
- 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
- Figures and Equations
- New Functions
- 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
- 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
- Network Properties
- Architecture
- Subobject Structures
- Functions
- Parameters
- Weight and Bias Values
- Other
- Subobject Properties
- Inputs
- Layers
- Outputs
- Targets
- Biases
- Input Weights
- Layer Weights
- Functions -- By Category
- Functions by Network Type
- Functions by Class
- Transfer Functions
- Transfer Function Graphs
- Reference Headings
- Functions -- Alphabetical List
- 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 9: Recurrent Networks
- Chapter 10: Adaptive Networks
- Chapter 11: Applications
- Block Set
- Transfer Function Blocks
- Net Input Blocks
- Weight Blocks
- Control Systems Blocks
- Block Generation
- Example
- Exercises
- Dimensions
- Variables
- Utility Function Variables
- Functions
- Code Efficiency
- Argument Checking
| | Getting Started | |