Collaborative architectures of fuzzy modeling

  • Authors:

  • Witold Pedrycz

  • Affiliations:

  • 1 Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Canada and Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

  • Venue:
  • WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
  • Year:
  • 2008

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Abstract

There are evident and profoundly articulated needs to deal with distributed sources of data (such as e.g., sensors and sensor networks, web sites, distributed databases). While recognizing limited accessibility of such data at a global level (which could be associated with technical constraints and/or privacy issues) and fully acknowledging benefits and potentials of collaborative processing, we introduce a concept of Collaborative Computational Intelligence (CI), and collaborative fuzzy models, in particular. Collaboration is realized in different ways by engaging a host of bidirectional interactions between all local processing sites (models) or by proceeding with unidirectional communication in which we establish some mechanisms of developing experience consistency of fuzzy modeling. We offer a coherent taxonomy of various schemes of interaction which in the sequel implies a certain development of a suite of algorithms. In this setting, we highlight a pivotal role of granular information in the establishing of the mechanisms of interaction. In the realm of collaborative fuzzy models and fuzzy modeling we elaborate on the concept of knowledge sharing. We also bring forward a concept of experience-consistent fuzzy system identification showing how fuzzy models built on a basis of limited data can benefit from taking advantage of the past experience conveyed in the form of previously constructed fuzzy models. Proceeding with a more detailed algorithmic framework, we elaborate on the key design issues concerning fuzzy rule-based systems which constitute a dominant category of fuzzy models. Collaboration invokes some mechanisms of aggregation and reconciliation of local findings. We emphasize that the resulting findings such as specific components of models can be quantified in terms of type-2 fuzzy sets - a pursuit which offers an interesting motivation behind this higher type of fuzzy sets