Ann

Purpose

Predictions based on Artificial Neural Network regression models.

Synopsis

[model] = ann(x,y,options);

[ypred] = ann(x,model,options);

[ypred] = ann(x,y,model,options);

[ypred] = ann(x,y, xt,yt, options);

Description

Build an ANN model from input X and Y block data using the optimal number of learning iterations (based on cross-validation) for specified number of layers and layer nodes. Alternatively, if a model is passed in ANN makes a Y prediction for an input test X block. The ANN model contains quantities (weights etc) calculated from the calibration data. If a model structure is passed in then these do not have to be re-calculated.

Options

options = a structure array with the following fields:

• display : [ 'off' |{'on'}] Governs display
• plots: [ {'none'} | 'final' ] governs plotting of results, and
• order: positive integer for polynomial order {default = 1}.
• waitbar : [ 'off' |{'auto'}| 'on' ] governs use of waitbar during analysis. 'auto' shows waitbar if delay will likely be longer than a reasonable waiting period.
• anntype : [{1} | 2] Number of hidden layers.
• nhid1 : [{2}] Number of nodes in first hidden layer.
• nhid2 : [{2}] Number of nodes in second hidden layer.
• learnrate : [0.125] ANN learning rate
• cvmethod : [{'con'} | 'vet' | 'loo' | 'rnd'] CV method OR [] for KS one-split
• cvsplits : [{5}] Number of CV subsets
• learncycles : [{20}] Iterations of the training cycle

See Also

modelselector