neural network

Robust design: Accounting for uncertainties in engineering

This thesis concerns optimization of structures considering various uncertainties. The overall objective is to find methods to create solutions that are optimal both in the sense of handling the typical load case and minimising the variability of the response, i.e. robust optimal designs.Traditionally optimized structures may show a tendency of being sensitive to small perturbations in the design or loading conditions, which of course are inevitable.

A machine learning approach in financial markets

In this work we compare the prediction performance of three optimized technical indicators with a Support Vector Machine Neural Network. For the indicator part we picked the common used indicators: Relative Strength Index, Moving AverageConvergence Divergence and Stochastic Oscillator. For the Support

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