Neural Network Toolbox 6.0.1

Product Description

• Introduction and Key Features
• Working with Neural Network Toolbox™
• Network Architectures
• Training and Learning Functions
• Simulink Support and Control System Applications
• Preprocessing and Postprocessing Functions

Training and Learning Functions

Training and learning functions are mathematical procedures used to automatically adjust the network's weights and biases. The training function dictates a global algorithm that affects all the weights and biases of a given network. The learning function can be applied to individual weights and biases within a network.

Neural Network Toolbox supports a variety of training algorithms, including several gradient descent methods, conjugate gradient methods, the Levenberg-Marquardt algorithm (LM), and the resilient backpropogation algorithm (Rprop). Algorithms can be accessed from the command line or via a training GUI, which shows a diagram of the network being trained, training algorithm choices, and stopping criteria values as the training progresses.

A suite of learning functions, including gradient descent, hebbian learning, LVQ, Widrow-Hoff, and Kohonen, is also provided.