Radial basis function network: Difference between revisions

Revision as of 18:13, 1 May 2009

Revision as of 06:16, 2 May 2009

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where the learning rate <math> \nu </math> is again taken to be 0.3. The training is performed with one pass through the 100 training points. The [[Mean squared error|rms error]] on a test set of 100 exemplars is 0.084, smaller than the unnormalized error. Normalization yields accuracy improvement. Typically accuracy with normalized basis functions increases even more over unnormalized functions as input dimensionality increases.

[[ImageFile:060803bChaotic chaoticTime timeSeries series predictionPrediction.pngsvg|thumb|350px|right|Figure 9: Normalized basis functions. The Logistic map (blue) and the approximation to the logistic map (red) as a function of time. Note that the approximation is good for only a few time steps. This is a general characterisitc of chaotic time series.]]

===Time series prediction===