抄録
This paper proposes to use membership functions (MSFs) in a form of piecewise linear functions for the fuzzy control rules. For determining a manipulated variable with an aid of fuzzy control so as to match a required output of a plant, tuning of fuzzy rules is needed. So far, this kind of tuning is performed by technique of trial and error. However, the tuning method needs much time for its tuning, and a suitable tuning cannot be always expected. So, we have tuned fuzzy rules based on a performance function in a neural network, where shapes of the MSF can be changed flexibly. Hence an expressiveness of the fuzzy reasoning is expanded. For its learning computation, a learning algorithm using coefficients modified by global searching (LACG) is derived. This LACG serves to avoid the local optimum in its numerical solution. An advantageous feature of the LACG is described with numerical examples.
