Soft computing based signal processing approaches for supporting modeling and control of engineering systems - a case study

Nowadays in solving engineering problems the processing of the information always involves some kind of measurement and signal processing tasks, as well. This fact has pressed experts focus more deeply on modeling and signal processing techniques and to apply non-classical, artificial intelligence, and soft computing based methods to overcome problems arising from the increased complexity of the today's systems. In this paper author shows that measurement and signal processing problems of now-a-days open new dimensions for the interpretation of the basic concepts of measurement and signal processing and make the reevaluation of these concepts necessary. Traditional methods fail in many cases to yield useful solutions, especially when measurement and signal processing problems reveal considerable complexity, involve a wide spectrum of various disciplines, and require a multitude of components and methods. The paper gives a brief overview of various imprecise computational methods and discusses their applicability to treat complex modeling, signal processing, and control problems.