The fuzzy rulebase consists of a set of antecedent-consequent linguistic rules of the form [Pg.332]

For the input and output fuzzy windows given in Figure 10.8 and 10.10, together with the fuzzy rulebase shown in Figure 10.9, determine [Pg.336]

If the numerieal strueture of the fuzzy rulebase does not give an aeeeptable response, then the values in eertain eells will need to be adjusted. [Pg.346]

The two seven set fuzzy input windows shown in Figure 10.8 gives a possible 7x7 set of control rules of the form given in equation (10.21). It is convenient to tabulate the two-dimensional rulebase as shown in Figure 10.9. [Pg.332]

Fuzzy inference is therefore the process of mapping membership values from the input windows, through the rulebase, to the output window(s). [Pg.335]

For the rulebase given in equation (10.52), the fuzzy max-min inferenee proeess is [Pg.340]

The 11 and 22 set rulebase simulations were undertaken using SIMULINK, together with the fuzzy logie toolbox for use with MATLAB. More details on the [Pg.341]

MATLAB Fuzzy Inference System (FIS) editor can be found in Appendix 1. Figure 10.16 shows the control surface for the 11 set rulebase fuzzy logic controller. [Pg.344]

Self-Organizing Fuzzy Logic Control (SOFLC) is an optimization strategy to create and modify the control rulebase for a FLC as a result of observed system performance. The SOFLC is particularly useful when the plant is subject to time-varying parameter changes and unknown disturbances. [Pg.344]

Fig. 10.16 Control surface for 11 set rulebase fuzzy logic controller. |

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