The Xfuzzy 3.0 development environment


Tuning stage

The tuning stage is usually one of the most complex tasks when designing fuzzy systems. The system behavior depends on the logic structure of its rule base and the membership functions of its linguistic variables. The tuning process is very often focused on adjusting the different membership function parameters that appear in the system definition. Since the number of parameters to simultaneously modify is high, a manual tuning is clearly cumbersome and automatic techniques are required. The two learning mechanisms most widely used are supervised and reinforcement learning. In supervised learning techniques the desired system behavior is given by a set of training (and test) input/output data while in reinforcement learning what is known is not the exact output data but the effect that the system has to produce on its environment, thus making necessary the monitoring of its on-line behavior.

Xfuzzy 3.0 environment currently contains one tool dedicated to the tuning stage: xfsl, which is based on the use of supervised learning algorithms. Supervised learning attempts to minimize an error function that evaluates the difference between the actual system behavior and its desired behavior defined by a set of input/output patterns. 



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   Last update: 1-7-2003 00:00:00