Classification of fusion topologies in hybrid soft computing and hard computing systems

Soft computing (SC) and hard computing (HC) methodologies are fused together in numerous industrial and other applications. The aim is typically to develop intelligent systems that would be straightforward to analyze, their behavior and stability could be predictable, and the computational burden would be no more than moderate. All these goals are particularly important in embedded real-time applications. This paper clarifies the vagueness related to the fusion of SC and HC methodologies that slows down the acceptance of soft computing outside the computational intelligence community. We categorize the different fusion topologies to 12 core classes and 6 supplementary classes, and discuss the characteristic features of the SC and HC constituents in practical hybrid implementations.