Fuzzy rulebase


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

This style of fuzzy conditional statement is often called a Mamdani -type rule, after Mamdani (1976) who first used it in a fuzzy rulebase to control steam plant.  [c.332]

Fig. 10.9 Tabular structure of a linguistic fuzzy rulebase. Fig. 10.9 Tabular structure of a linguistic fuzzy rulebase.
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  [c.336]

Fig. 10.18 Tabular structure of a numerical fuzzy rulebase. Fig. 10.18 Tabular structure of a numerical fuzzy rulebase.
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.  [c.346]

A fuzzy logic controller has input and output fuzzy windows as shown in Figure 10.39. The fuzzy rulebase is given in Figure 10.40. If defuzzification is by the centre of area method, calculate crisp control signals u t) when the error e t) and the rate of change of error ce t) have the following values  [c.373]

Fig. 10.40 Fuzzy rulebase for Example 10.7. Fig. 10.40 Fuzzy rulebase for Example 10.7.
Fig. 10.42 Fuzzy rulebase for Example 10.8. Fig. 10.42 Fuzzy rulebase for Example 10.8.
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.  [c.332]

Fig. 10.8 Seven set fuzzy input windows for error (e) and rate of change of error ice). Assume that a certain rule in the rulebase is given by equation (10.22) OR IF e is A AND ce is B THEN u = C Fig. 10.8 Seven set fuzzy input windows for error (e) and rate of change of error ice). Assume that a certain rule in the rulebase is given by equation (10.22) OR IF e is A AND ce is B THEN u = C
Fuzzy inference is therefore the process of mapping membership values from the input windows, through the rulebase, to the output window(s).  [c.335]

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  [c.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.  [c.344]

Fig. 10.16 Control surface for 11 set rulebase fuzzy logic controller. Fig. 10.16 Control surface for 11 set rulebase fuzzy logic controller.
If the fuzzy inference system has inputs xi and X2 and output /as shown in Figure 10.31, then a first-order TSK rulebase might be  [c.363]

Using the fuzzy logie approaeh suggested by Johnson and Pieton (1995), four, three set input windows (one for eaeh state variable) and one, three set output window has been seleeted as shown in Figure 10.14. Using heuristie knowledge from broom-balaneing experiments, the following Mamdani-type rulebase was eon-strueted  [c.340]

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

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.  [c.344]

Inverted Pendulum, Fuzzy Logic Controller Design The fuzzy logie eontroller used for the inverted pendulum problem has four input windows and one output window as shown in Figure 10.14. One version of the eontroller has a Mamdani-type rulebase eonsisting of 11 rules as given in equation (10.52). The MATLAB Fuzzy Inferenee System (FIS) Editor ean be entered by typing at the MATLAB prompt  [c.418]


See pages that mention the term Fuzzy rulebase : [c.332]    [c.346]    [c.346]    [c.374]    [c.362]    [c.372]   
Advanced control engineering (2001) -- [ c.332 , c.336 , c.374 ]