Description: This program demonstrates some function approximation capabilities of a Radial Basis Function Network.
The user supplies a set of training points which represent some \"sample\" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given \"sample\" points. -This program demonstrates some function a pproximation capabilities of a Radial Basis Fu nction Network. The user supplies a set of train ing points which represent some "sample" point s for some arbitrary curve. Next, the user specifies the number of equally spaced Response centers and the variance for the netwo rk. Using the training samples, the weights multiplying each of the Gaussian ba sis functions arecalculated using the pseudo - inverse (yielding the minimum least-squares s middleware). The resulting network is then used to approximate the function between the given "sa mple "points. Platform: |
Size: 18685 |
Author:陈伟 |
Hits:
Description: This program demonstrates some function approximation capabilities of a Radial Basis Function Network.
The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points. -This program demonstrates some function a pproximation capabilities of a Radial Basis Fu nction Network. The user supplies a set of train ing points which represent some "sample" point s for some arbitrary curve. Next, the user specifies the number of equally spaced Response centers and the variance for the netwo rk. Using the training samples, the weights multiplying each of the Gaussian ba sis functions arecalculated using the pseudo- inverse (yielding the minimum least-squares s middleware). The resulting network is then used to approximate the function between the given "sa mple "points. Platform: |
Size: 18432 |
Author:陈伟 |
Hits:
Search - train Pseudo inverse