The Xfuzzy 3 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.

The Xfuzzy 3 environment includes four tools for this design stage: xfdm and xftsp are knowledge acquisition tools. The first one allows obtaining the structure of inference systems used as fuzzy approximators or classifiers, while the second one is specially focused on time series prediction applications. xfsl is a parameter adjustment tool based on the use of supervised learning algorithms. In supervised learning techniques, the desired behavior of the system is described by a set of training (and test) patterns. Supervised learning attempts to minimize an error function that evaluates the difference between the actual system behavior and its desired behavior defined by the set of input/output patterns. Finally, xfsp is a simplification tool that allows reducing the number of membership functions and compacting the rules bases of a fuzzy system to facilitate its software or hardware implementation and to increase its linguistic interpretability.

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