A Taxonomy of Models

Abstract

The paradigm of hybrid computing deals with the fusion of numerous computing methods, where each method contributes towards the removal of the limitations of the other methods. In this manner we try to magnify the advantages of all methods and remove the limitations of each of the methods. The hybridization deals with the fusion of Artificial Neural Networks, Evolutionary Algorithms, Fuzzy Logic along with heuristics. The large number of models of all of these present a large set of options using which the various methods may be fused. The application areas present further options where each of the models may be customized as per the problem requirements. The same is true with adaptive computing as well, where different adaptation techniques or adaptive algorithms may be used for a better system performance. Complexity largely limits the millions of possibilities of creating different types of hybrid and adaptive systems, which is further emphasized by a limited computation and data availability. This chapter gives a broad overview of the systems presenting the immense varieties in which the systems can be engineered. The basic question that hence arises is how to make a judicious choice amongst these immense options.