Simulation of stochastic elements in railway systems using self-learning processes

Article Abstract:

Research describing the use of self-learning processes to simulate stochastic elements in railway systems is presented. Human operators select and apply the deterministic rules which are followed by railway traffic, and may therefore vary between different circumstances. Systems capable of reproducing these and acting on self-learning processes are tested.

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Model combination in neural-based forecasting

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Different ways of combining neural predictive models using Gaussian radical basis function are proposed. These models can be used for efficient optimal decision-making.

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Neuro-dynamic trading methods

Article Abstract:

A study on methods and models of neurocomputing, which are useful in automated trading systems and investment analysis, is presented.

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