Implicit de-noising in hybrid recurrent nets for meta knowledge abduction

Several financial, physical and biological phenomena exhibit random oscillatory and cyclic behaviour generated by a set of casual parameters referred to as meta knowledge (M-K). Given these behaviour time trajectories, recurrent hybrid nets are used to determine the time derivatives of the behaviour and using these to abduct the values of the casual parameters in real time. The recurrent hybrid nets used possess de-noising properties which can set by the designer. This paper investigates sensitivity to noise of 3 meta knowledge abduction algorithms developed in earlier papers using simulation. The effect of measurement noise on the estimation accuracy is considered when the behaviour trajectories are corrupted with random noise. Noise is simulated using random number generator with zero mean and added to the simulated system behaviour. Analysis of the simulation results show varying abilities of the algorithms to cope with the noise perturbations. In some instances high prediction robustness were achieved, other simulations showed high sensitivity to noise.