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imported>Scott (Created page with "===Purpose=== Predictions based on Artificial Neural Network regression models. ===Synopsis=== : [model] = ann(x,y,options); : [ypred] = ann(x,model,options); : [ypred] = a...") |
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===Purpose=== | ===Purpose=== | ||
Predictions based on Artificial Neural Network regression models. | Predictions based on Artificial Neural Network (ANN) regression models. | ||
===Synopsis=== | ===Synopsis=== | ||
| Line 12: | Line 12: | ||
===Description=== | ===Description=== | ||
Build an ANN model from input X and Y block data using the | 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 [http://en.wikipedia.org/wiki/Rprop] for further details. ANN is implemented using the Encog package [http://www.heatonresearch.com/encog]. | ||
===Options=== | ===Options=== | ||
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* '''display''' : [ 'off' |{'on'}] Governs display | * '''display''' : [ 'off' |{'on'}] Governs display | ||
* '''plots''': [ {'none'} | 'final' ] governs plotting of results, and | * '''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. | * '''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. | * '''anntype''' : [{1} | 2] Number of hidden layers. | ||
| Line 25: | Line 24: | ||
* '''nhid2''' : [{2}] Number of nodes in second hidden layer. | * '''nhid2''' : [{2}] Number of nodes in second hidden layer. | ||
* '''learnrate''' : [0.125] ANN learning rate | * '''learnrate''' : [0.125] ANN learning rate | ||
* ''' | * '''maxseconds''' : [{30}] Maximum duration of ANN training. | ||
* ''' | * '''terminalrmse''' : [0.005] Termination RMSE value for ANN iterations. | ||
* ''' | * '''terminalrmserate''' : [1.e-6] 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 SVM model. 'pca' uses a simple PCA model to compress the information. 'pls' uses either a pls or plsda model (depending on the svmtype). Compression can make the SVM 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=== | ===See Also=== | ||
[[modelselector]] | [[modelselector]] | ||
Revision as of 17:03, 14 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].
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 : [{2}] Number of nodes in second hidden layer.
- learnrate : [0.125] ANN learning rate
- maxseconds : [{30}] Maximum duration of ANN training.
- terminalrmse : [0.005] Termination RMSE value for ANN iterations.
- terminalrmserate : [1.e-6] 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 SVM model. 'pca' uses a simple PCA model to compress the information. 'pls' uses either a pls or plsda model (depending on the svmtype). Compression can make the SVM 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.