Fuzzy rulebase

The fuzzy rulebase consists of a set of antecedent-consequent linguistic rules of the form... 

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. 

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... 

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. 

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. 

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). 

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

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... 

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. 

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. 

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... 

A rule based approach to process control has for many years provided an alternative to traditional methods in the form of fuzzy logic control (8,9). Since the advent of expert systems, rulebases have been used for fault diagnosis [10], to advise operators (11) 9 to aid control engineers when installing PID controllers (12), to provide expert on-line tuning for PID controllers (13), and to control processes without the use of fuzzy logic (14,15). 

If we define a process surface as a hyper-plane derived from a multiple set of process inputs/output relationships, it will be possible to relate inputs to outputs, and tune the processor by altering the rulebase and comparing the effect on the process surface. Each point on the plane has its coordinate, [x y z], defining the position within the envelope relating inputs, [x y] to output [z]. This means that the fuzzy processor can be tuned by shaping the process surface, rather than by adjusting numerical gains. 

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