Radial Basis Approximation

This example uses the NEWRB function to create a radial basis network that approximates a function defined by a set of data points.

Define 21 inputs P and associated targets T.

X = -1:.1:1;
T = [-.9602 -.5770 -.0729  .3771  .6405  .6600  .4609 ...
      .1336 -.2013 -.4344 -.5000 -.3930 -.1647  .0988 ...
      .3072  .3960  .3449  .1816 -.0312 -.2189 -.3201];
title('Training Vectors');
xlabel('Input Vector P');
ylabel('Target Vector T');

We would like to find a function which fits the 21 data points. One way to do this is with a radial basis network. A radial basis network is a network with two layers. A hidden layer of radial basis neurons and an output layer of linear neurons. Here is the radial basis transfer function used by the hidden layer.

x = -3:.1:3;
a = radbas(x);
title('Radial Basis Transfer Function');
xlabel('Input p');
ylabel('Output a');

The weights and biases of each neuron in the hidden layer define the position and width of a radial basis function. Each linear output neuron forms a weighted sum of these radial basis functions. With the correct weight and bias values for each layer, and enough hidden neurons, a radial basis network can fit any function with any desired accuracy. This is an example of three radial basis functions (in blue) are scaled and summed to produce a function (in magenta).

a2 = radbas(x-1.5);
a3 = radbas(x+2);
a4 = a + a2*1 + a3*0.5;
title('Weighted Sum of Radial Basis Transfer Functions');
xlabel('Input p');
ylabel('Output a');

The function NEWRB quickly creates a radial basis network which approximates the function defined by P and T. In addition to the training set and targets, NEWRB takes two arguments, the sum-squared error goal and the spread constant.

eg = 0.02; % sum-squared error goal
sc = 1;    % spread constant
net = newrb(X,T,eg,sc);
NEWRB, neurons = 0, MSE = 0.176192

To see how the network performs, replot the training set. Then simulate the network response for inputs over the same range. Finally, plot the results on the same graph.


X = -1:.01:1;
Y = net(X);

hold on;
hold off;