Neural Network Toolbox

Backpropagation

Introduction	Introduces the chapter and provides information on additional resources
Fundamentals	Discusses the architecture, simulation, and training of backpropagation networks
Faster Training	Discusses several high-performance backpropagation training algorithms
Speed and Memory Comparison	Compares the memory and speed of different backpropagation training algorithms
Improving Generalization	Discusses two methods for improving generalization of a network--regularization and early stopping
Preprocessing and Postprocessing	Discusses preprocessing routines that can be used to make training more efficient, along with techniques to measure the performance of a trained network
Sample Training Session	Provides a tutorial consisting of a sample training session that demonstrates many of the chapter concepts
Limitations and Cautions	Discusses limitations and cautions to consider when creating and training perceptron networks
Summary	Provides a consolidated review of the chapter concepts

User's Guide Introduction