Fig. 10.11 Clipped fuzzy output window due to fuzzy inference. |

Fig. A1.5 Main menu of Fuzzy Inference System (FIS) editor. |

Fuzzy Logic Control The apphcation of fuzzy logic to process control requires the concepts of Fuzzy rules and fuzzy inference. A fuzzy rule, also known as a fuzzy IF-THEN statement, has the form [Pg.735]

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

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]

The ANFIS neurofuzzy controller was implemented by Jang (1993) and employs a Takagi-Sugeno-Kang (TSK) fuzzy inference system. The basic ANFIS architecture is shown in Figure 10.31. [Pg.362]

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 [Pg.363]

Jang, J.S.R. (1993) ANFIS Adaptive Network-based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics, 23, pp. 665-685. [Pg.430]

Actuation subsystem 326 Actuator saturation 91 Adaptive linear element (ADLINE) 347 Adaptive network based fuzzy inference system (ANFIS) 362 [Pg.433]

MATLAB 109,133,161,341 command window control system toolbox 382 editor/debugger 383 fuzzy inference system (FIS) editor 344 robust control toolbox 320 toolboxes 380 Matrix 424 [Pg.442]

Equation (10.23) is referred to as the max-min inference process or max-min fuzzy reasoning. [Pg.333]

Defuzzification is the procedure for mapping from a set of inferred fuzzy control signals contained within a fuzzy output window to a non-fuzzy (crisp) control signal. The centre of area method is the most well known defuzzification technique, which in linguistic terms can be expressed as [Pg.335]

© 2019 chempedia.info