Neural Network Toolbox Release Notes |
New Features
This section introduces the new features and enhancements added in the Neural Network Toolbox 4.0 since the Neural Network Toolbox 3.0.1 (Release 11.0).
Control System Applications
A new Control Systems chapter of the Neural Network Toolbox User's Guide presents three practical control systems applications:
Visual Interface
A visual interface has been added to the toolbox. This interface allows you to:
To open the Network/Data Manager window, type nntool
.
Note This visual interface is not supported on the HP and IBM platforms. You can achieve the same functionality by using the MATLAB command window. |
New Training Functions
The Neural Network Toolbox now has four training algorithms that apply weight and bias learning rules. One algorithm applies the learning rules in batch mode. Three algorithms apply learning rules in three different incremental modes:
trainb
- Batch training function
trainc
- Cyclical order incremental training function
trainr
- Random order incremental training function
trains
- Sequential order incremental training function
All four functions present the whole training set in each epoch (pass through the entire input set).
These new training functions are relatively fast because they generate M-code. The functions trainb
, trainc
, trainr
, and trains
all generate a temporary M-file consisting of specialized code for training the current network in question.
Design of General Linear Networks
The function newlind
now allows you to design linear networks with multiple inputs, outputs, and input delays.
Improved Early Stopping
Early stopping can now be used in combination with Bayesian regularization. In some cases this can improve the generalization capability of the trained network.
Generalization and Speed Benchmarks
Generalization benchmarks comparing the performance of Bayesian regularization and early stopping are provided. Also included are speed benchmarks, which compare the speed of convergence of the various training algorithms on a variety of problems in pattern recognition and function approximation. These benchmarks can aid you in selecting the appropriate algorithm for your problem.
Demonstration of a Sample Training Session
A new demonstration that illustrates a sample training session is included in the "Backpropagation" chapter of the Neural Network Toolbox User's Guide. A sample training session script is also provided. You can modify this script to fit your problem.
Neural Network Toolbox 4.0.1 Release Notes | Upgrading from an Earlier Release |