Abstract In this work, metamodel-based robust optimization is performed using measured scatter of noise variables. Principal component analysis used to describe the input linearly uncorrelated principal components. Some these components follow a normal probability distribution, others however deviate from distribution. that case, for more accurate description material scatter, multimodal distri...

The first consideration in predictive ecological modelling is the selection of appropriate input variables. Numerous variables can however be involved and most of them cannot be omitted without a significant loss of information. The collection of field data on the other hand is both time-consuming and expensive. Therefore, rigorous methods are needed to detect which variables are essential and ...

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In this chapter, we propose a new method to measure the importance of input variables and to examine the effect of the input variables on other components. We applied the method to competitive learning, in particular, self-organizing maps, to demonstrate the performance of our method. Because our method is based upon our information-theoretic competitive learning, it is easy to incorporate the ...

We propose a new computational method of input selection for stock market forecasting with neural networks. The method results from synthetically considering the special feature of input variables of neural networks and the special feature of stock market time series. We conduct the experiments to compare the prediction performance of the neural networks based on the different input variables b...