Virtual Computational Chemistry Laboratory

Input data Output results Example List of key words



TYPE

Keyword of Integer Type

Indicate type of neural network to be used in calculations.

standard {0} -- feed-forward neural network trained according to back-propagation TRAINING with pre-determined architecture and number of hidden layers;
CASCOR (QuickProp1) {1} -- Cascade Correlation algorithm, updated from the original version of S. Falhman by Vasily V. Kovalishyn. The training of weights is done using the original version of QuickProp algorithm.
CASCOR (TRAINING)  {2} -- Original implementation (unpublished, Tetko, 1997) of the Cascade Correlation algorithm with training of weights selected according to  TRAINING option.
The structure of  Cascade Correlation is completely different from standard back-propagation algorithm. This algorithm begins training with no hidden neurons that are added in process of training. Thus, the structure of Cascade Correlation nets grows during with time of network training.

The parameter NEURONS for Cascade Correlation network should contain only one value that indicate maximum allowed number of hidden neurons. Usually, NEURONS=100 hidden neurons is a good choice. The training of this algorithm is terminated if RMSE error for the validation set does not decrease after 20 new hidden neurons were added to network or LIMIT criterion was satisfied.

The default value is {0}.

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