Concepts and Design of Granular Models: Emerging Constructs of Computational Intelligence
In spite of their striking diversity, numerous tasks and architectures of intelligent systems such as those permeating multivariable data analysis (e.g., time series, spatio-temporal, and spatial dependencies), decision-making processes along with their underlying models, recommender systems and others exhibit two evident commonalities. They promote (a) human centricity and (b) vigorously engage perceptions (rather than plain numeric entities) in the realization of the systems and their further usage. Information granules play a pivotal role in such settings. Granular Computing delivers a cohesive framework supporting a formation of information granules and facilitating their processing. We exploit an essential concept of Granular Computing: an optimal allocation of information granularity, which helps endow constructs of intelligent systems with a much-needed conceptual and modeling flexibility. The study elaborates in detail on the three representative studies. In the first study being focused on the Analytic Hierarchy Process (AHP) used in decision-making, we show how an optimal allocation of granularity helps improve the quality of the solution and facilitate collaborative activities (e.g., consensus building) in models of group decision-making. The second study concerns a formation of granular logic descriptors on a basis of a family of logic descriptors Finally, the third study focuses on the formation of granular fuzzy neural networks architectures aimed at the formation of granular logic mappings.
decision-making human centricity granular computing optimal allocation of information granularity design of information granules principle of justifiable granularity