On the Parameter Optimization of Fuzzy Inference Systems

Nowadays, more engineering systems are using some kind of Artificial Intelligence (AI) for the development of their processes. Some well-known AI techniques include artificial neural nets, fuzzy inference systems, and neuro-fuzzy inference systems among others. Furthermore, many decision-making applications base their intelligent processes on Fuzzy Logic; due to the Fuzzy Inference Systems (FIS) capability to deal with problems that are based on user knowledge and experience. Also, knowing that users have a wide variety of distinctiveness, and generally, provide uncertain data, this information can be used and properly processed by a FIS. To properly consider uncertainty and inexact system input values, FIS normally use Membership Functions (MF) that represent a degree of user satisfaction on certain conditions and/or constraints. In order to define the parameters of the MFs, the knowledge from experts in the field is very important. This knowledge defines the MF shape to process the user inputs and through fuzzy reasoning and inference mechanisms, the FIS can provide an “appropriate" output. However an important issue immediately arises: How can it be assured that the obtained output is the optimum solution? How can it be guaranteed that each MF has an optimum shape? A viable solution to these questions is through the MFs parameter optimization. In this Paper a novel parameter optimization process is presented. The process for FIS parameter optimization consists of the five simple steps that can be easily realized off-line. Here the proposed process of FIS parameter optimization it is demonstrated by its implementation on an Intelligent Interface section dealing with the on-line customization / personalization of internet portals applied to E-commerce.

Authors:

• Erika Martinez Ramirez
• Rene V. Mayorga

Keywords:

• Artificial Intelligence
• Fuzzy Logic
• Fuzzy InferenceSystems
• Nonlinear Optimization.

References:

[1] Jang J.-S. R., Sun C.-T. Mizutani E. Neuro-Fuzzy and Soft Computing:
A computational approach to learning and machine intelligence. Matlab
Curriculum Series. Edit. Prentice Hall. 1997.
[2] Martinez E. Mayorga R. V., "An Architecture for the Coupling of
Intelligent Computer Interfaces with Intelligent Systems: An Online
Internet Portals Customization Application. Proceedings 4th
ANIROB/IEEE-RAS Intl. Symposium on Robotics and Automation,
Queretaro, Mexico, August, 25-27, 2004
[3] Mayorga R.V. "Towards Computational Sapience (Wisdom) and
Metabotics: Intelligent / Sapient (Wise) Decision / Control, Systems, and
MetaBots", Proceedings 4th ANIROB/IEEE-RAS Intl. Symposium on
Robotics and Automation, Queretaro, Mexico, August, 25-27, 2004
[4] Mayorga R.V. "Towards Computational Sapience (Wisdom): A
Paradigm for Sapient (Wise) Systems", Proceedings International
Conference on Knowledge Intensive Multi-Agent Systems, KIMAS-03,
Cambridge, MA, USA, September 30 - October 4, 2003.
[5] Mayorga R. V. "A Metabotics Paradigm for the Wise Design and
Operation of a Human-Computer Interface, Proceedings 2nd
ANIROB/IEEE-RAS Intl. Symposium on Robotics and Automation,
Monterrey, Mexico, November 10-12, 2000.
[6] Stewart, J., Calculus, Second Edition. Brooks/Cole Publishing
Company. Pacific Grove, California.1991.
[7] Matlab Help Tutorials on Fuzzy, Optimization and Symbolic Math
Toolboxes.
[8] Yon J-h, Yang S-m, Jeon H-T. "Structure Optimization of Fuzzy-Neural
Network Using Rough Set Theory" in 1999 IEEE International Fuzzy
Systems Conference Proceedings. Korea, 1999.