engleski

Structure Optimization of Neural Networks in Relation to Underlying Data

Optimization of neural network topology has been one of the most important problems since neural network came in front as a method for prediction, classification, and association. Number of heuristics formulas for determining the number of hidden units were developed (Masters,T., 1993, Marcek, D., 1997), and some algorithms for structure optimization were suggested, such as cascading, pruning, A* algorithm, and others. The connection between optimization techniques and underlying data in the model is not investigated enough. The paper deals with the influence of variable selection and statistics of input and output variables to several algorithms for structure optimization. Principal component analysis and analysis of variance among other statistical tests are conducted in stock return prediction models. The predictive power of neural networks is captured, and also the sensitivity of the dependent variables to changes in the inputs.

structure optimization of neural networks; cascading; pruning; variable selection; principal component analysis

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