Neural Network Toolbox

Summary

The ADALINE (Adaptive Linear Neuron networks) networks discussed in this chapter are similar to the perceptron, but their transfer function is linear rather than hard-limiting. They make use of the LMS (Least Mean Squares) learning rule, which is much more powerful than the perceptron learning rule. The LMS or Widrow-Hoff learning rule minimizes the mean square error and, thus, moves the decision boundaries as far as it can from the training patterns.

In this chapter, we design an adaptive linear system that responds to changes in its environment as it is operating. Linear networks that are adjusted at each time step based on new input and target vectors can find weights and biases that minimize the network's sum-squared error for recent input and target vectors.

Adaptive linear filters have many practical applications such as noise cancellation, signal processing, and prediction in control and communication systems.

This chapter introduces the function adapt, which changes the weights and biases of a network incrementally during training.

Multiple Neuron Adaptive Filters

Figures and Equations

© 1994-2005 The MathWorks, Inc.