Concepts of evolvable and knowledge-consistent fuzzy models

In this study, we augment the highly impressive record of developments of fuzzy models by bringing the ideas of evolvable and knowledge-consistent fuzzy modeling. More often than in the past, we are exposed to highly distributed data reflecting some temporal or spatial variability of the problem. Owing to some non-technical reasons (e.g., data privacy and security) or existing technical constraints, the models built locally cannot take advantage of the data available elsewhere. Instead one could be provided with some more abstract entities such as information granules that are reflective of the knowledge conveyed by some other models which could be effectively shared. The two main categories of design schemes discussed here demonstrate the effect of achieving knowledge consistency which augments the existing paradigm of fuzzy modeling. In the first one, we are concerned with sharing temporal knowledge where the models are formed for temporal data available in successive time slices pertinent to the problem at hand and the available temporal knowledge (captured in terms of the structure and parameters of the models) whose usage incorporates the factor of time. In this sense, the resulting fuzzy models become highly evolvable modeling architectures. The spatial nature of knowledge is associated with fuzzy models which are constructed on a basis of data pertinent to some local regions (such as sections of wireless sensor networks, sales regions, etc.). While the introduced conceptual developments are of substantial level of generality, the study will focus on a family of rule-based fuzzy models to illustrate the ensuing algorithmic aspects of the fundamental concepts.