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** Adaptive network based fuzzy inference **

** Adaptive network based fuzzy inference system **

** Adaptive neuro-fuzzy inference system **

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]

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]

The rules of a Fuzzy Inference System link observations on the space of observed variables to singletons or disjunctions of singletons from the frame of discernment 17. It means that a rule can be expressed by [Pg.230]

ANFIS—adaptive neuro-fuzzy inference system [Pg.462]

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

Fault Detection Based on Fuzzy Inference Systems [Pg.228]

The Adaptive Network based Fuzzy Inference System (ANFIS) [Pg.362]

Figure 8.23 Adaptive neuro-fuzzy inference system. |

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

Fig. 10.31 TheAdaptive Network based Fuzzy Inference System (ANFIS) architecture (after Craven). |

Jang, J.S.R., ANFIS Adaptive-network-based fuzzy inference systems, IEEE Trans. Syst. Man [Pg.250]

M.M. Gupta J. Qi 1991. Theory of T-norms and fuzzy inference methods, Fuzzy Sets and Systems. 40 431-450. [Pg.724]

In order to combine the conclusions of several such Fuzzy Inference Systems, they will be translated into belief structures, according to the method proposed in [22]. Each Fuzzy Inference System presented Figure 12 is the association of fuzzification functions (providing a numerical evaluation of the membership of a variable to fuzzy sets) and of rules linking these observations to different classes which can be states or disjunctions of states of the process. [Pg.230]

In Example 8.9, the performance of this adaptive neuro-fuzzy inference system is demonstrated for classification of two-dimensional data. [Pg.331]

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]

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]

In this paper we describe the application of an adaptive network based fuzzy inference system (ANFIS) predictor to the estimation of the product compositions in a binary methanol-water continuous distillation column from available on-line temperature measurements. This soft sensor is then applied to train an ANFIS model so that a GA performs the searching for the optimal dual control law applied to the distillation column. The performance of the developed ANFIS estimator is further tested by observing the performance of the ANFIS based control system for both set point tracking and disturbance rejection cases. [Pg.466]

The results of this rule-base operation are then post-processed using a fuzzy inference engine to obtain the final prediction. [Pg.193]

Another important area of application is approximate reasoning based on fuzzy logic. Fuzzy inferences are applied in analytical expert systems or for controlling chemical or biotechnological reactors. [Pg.333]

See also in sourсe #XX -- [ Pg.165 , Pg.171 ]

** Adaptive network based fuzzy inference **

** Adaptive network based fuzzy inference system **

** Adaptive neuro-fuzzy inference system **

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