Ann

Purpose

Predictions based on Artificial Neural Network (ANN) regression models.

Synopsis

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

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

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

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

Description

Build an ANN model from input X and Y block data using the 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. When a model structure is passed in to ANN then these weights do not need to be calculated.

There are two implimentations of ANN available referred to as 'BPN' and 'Encog'.

BPN is a feedforward ANN using backpropagation training and is implemented in Matlab.

Encog is a feedforward ANN using Resilient Backpropagation training. See Rprop for further details.

Encog is implemented using the Encog framework Encog provided by Heaton Research, Inc, under the Apache 2.0 license. Further details of Encog Neural Network features are available at Encog Documentation. BPN is the ANN version used by default but the user can specify the option 'algorithm' = 'encog' to use Encog instead. Both implementations should give similar results but one may be faster than the other for different datasets. BPN is currently the only version which calculates RMSECV. When building an ANN model using:

Training Termination

BPN determines the optimal number of learning iteration cycles by selecting the minumum RMSEP for a test subset over a range of learning iterations.

Encog training will terminate whenever either or a) RMSE becomes smaller than option 'terminalrmse', or b) the rate of improvement of RMSE per 100 iterations becomes less than option 'terminalrmserate', or c) time exceeds option 'maxseconds' (though results are not optimal if training is stopped prematurely by this time limit). Note these RMSE values refer to the internal preprocessed and scaled y values.

Options

options = a structure array with the following fields:

• display : [ 'off' |{'on'}] Governs display
• plots: [ {'none'} | 'final' ] governs plotting of results, and
• waitbar : [ 'off' |{'auto'}| 'on' ] governs use of waitbar during analysis. 'auto' shows waitbar if delay will likely be longer than a reasonable waiting period.
• nhid1 : [{2}] Number of nodes in first hidden layer.
• nhid2 : [{0}] Number of nodes in second hidden layer.
• learnrate : [0.125] ANN backpropagation learning rate (bpn only).
• learncycles : [20] Number of ANN learning iterations (bpn only).
• terminalrmse : [0.05] Termination RMSE value (of scaled y) for ANN iterations (encog only).
• terminalrmserate : [1.e-9] Termination rate of change of RMSE per 100 iterations (encog only).
• maxseconds : [{20}] Maximum duration of ANN training in seconds (encog only).
• preprocessing: {[] []} preprocessing structures for x and y blocks (see PREPROCESS).
• compression: [{'none'}| 'pca' | 'pls' ] type of data compression to perform on the x-block prior to calculaing or applying the ANN model. 'pca' uses a simple PCA model to compress the information. 'pls' uses a pls model. Compression can make the ANN more stable and less prone to overfitting.
• compressncomp: [1] Number of latent variables (or principal components to include in the compression model.
• blockdetails: [ {'standard'} | 'all' ], extent of predictions and residuals included in model, 'standard' = only y-block, 'all' x- and y-blocks.

See Also

modelselector