Ann: Difference between revisions
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* '''terminalrmserate''' : [1.e-9] Termination rate of change of RMSE per 100 iterations. | * '''terminalrmserate''' : [1.e-9] Termination rate of change of RMSE per 100 iterations. | ||
* '''preprocessing''': {[] []} preprocessing structures for x and y blocks (see PREPROCESS). | * '''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 | * '''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. | * '''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. | * '''blockdetails''': [ {'standard'} | 'all' ], extent of predictions and residuals included in model, 'standard' = only y-block, 'all' x- and y-blocks. | ||
Revision as of 15:45, 18 November 2013
Purpose
Predictions based on Artificial Neural Network (ANN) 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 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. The ANN is a Feedforward ANN with Resilient Backpropagation training. See [1] for further details. ANN is implemented using the Encog package [2] provided by Heaton Research, Inc, under the Apache 2.0 license.
When building an ANN model the training will terminate whenever either a) time exceeds option maxseconds, or b) RMSE becomes smaller than option terminalrmse, or c) the rate of improvement of RMSE per 100 iterations becomes less than option terminalrmserate. 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.
- anntype : [{1} | 2] Number of hidden layers.
- nhid1 : [{2}] Number of nodes in first hidden layer.
- nhid2 : [{0}] Number of nodes in second hidden layer.
- maxseconds : [{20}] Maximum duration of ANN training (seconds).
- terminalrmse : [0.05] Termination RMSE value for ANN iterations (RMSE of scaled y).
- terminalrmserate : [1.e-9] Termination rate of change of RMSE per 100 iterations.
- 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.