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Future Challenges in Soft Computing: Theory? Computational Paradigms? Applications?

Signal modeling has evolved to be an important aspect of digital signal processing which has been successfully used to solve variety of problems in communication, control, biomedicine, geophysics, atmospheric science, and this list could continue. Signal models, which could be either parametric (with an assumption of certain functional form) or nonparametric techniques, entail efficient mathematical representation of data or signal so as to extract desirable information from either the signal or the system that generates it. Since the early work of Yule (which led to the development of Yule-Walker equations), several signal models have been developed and successfully used in various problems which include spectral analysis, speech processing, system identification, signal filtering and prediction. Though several signal models are available, both the AR and ARMA models are widely used in many application areas as they possess some desirable properties. Consequently, a lot of research efforts have been concentrated on these two models since the last three decades.