## Radial BASIS Neural Network MATLAB

CHAPTER 4 : RADIAL BASIS NETWORK IMPLEMENTATION IN MATLAB

1. LEARNING RULES

We will define a learning rule as a procedure for modifying the weights and biases of a network. (This procedure may also be referred to as a training algorithm.) The learning rule is applied to train the network to perform some particular task. Learning rules in the MATLAB toolbox fall into two broad categories: supervised learning and unsupervised learning.

Those two categories were described in detail in previous chapter. The algorithm has been developed using supervised learning.

In supervised learning, the learning rule is provided with a set of examples (the training set) of proper network behaviour: where is an input to the network, and is the corresponding correct (target) output. As the inputs are applied to the network, the network outputs are compared to the targets. The learning rule is then used to adjust the weights and biases of the network in order to move the network outputs closer to the targets. The Radial Basis Function learning rule falls in this supervised learning category.

2. LINEAR CLASSIFICATION

Linear networks can be trained to perform classification with the function NEWRB. NEWRB Design a radial basis network. Radial basis networks can be used to approximate functions. NEWRB adds neurons to the hidden layer of a radial basis network until it meets the specified mean squared error goal.

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