To use fuzzy controllers for automization tasks appropriate fuzzy sets and fuzzy rules have to be defined. This can be difficult in some domains, and the resulting controller has to be tuned. Neuro--fuzzy models can help in this tuning process by adapting fuzzy sets and creating fuzzy rules

into the next neuro-fuzzy control having the physical meaning of the speed. Both variables are expressed as dependences of the number of encoder impulses delivered during the sampling time. A second step is the design of a neuro-fuzzy controller for both the control loops. The ANFIS method uses training data

Neuro-fuzzy systems, have recently been applied to the problem of Call Admission Control (CAC) in ATM networks. Their advantage as opposed to classical schemes, based on the modeling of sources connected to the network, is their capability to learn the behavior of data sources through training

the acquisition of the neuro-fuzzy inverse-model of the system with some practical questions like the acquisition of a "good" training data set, and the choice of actuator significant variables. The extracted inverse model and its learning capabilities are used to design the actuator position controller

motivation) analysis of variance (ANOVA) with repeated measures on the motivation variable was performed on the data. 3 This approach involved taking the scores on the seven motivation subscales and incorporating them into the Type of Student X Gender design as a repeated measure (BMDP; Dixon, Results

of fuzzy control. Popular aggregation and defuzzification methods (max-min, sum-product, MOM and COG) are first shown to satisfy these constraints, and then three linearization methods are presented. Linearization methods are used to reorder fuzzy rules base such that a reordered rule base would result

between income and vulnerability, and it suggests that measures designed to highlight a skewed distribution would confirm the notion that the poorest communities within a country would face similar resource deficiencies when it comes to protecting themselves. Some other explanatory variables

Abstract | This review provides an introduction to Sup-

. Magnet’s link estimator addresses the inaccuracies in link estimation by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, Magnet addresses link dynamics by using the Trickle algorithm for control traffic

publicly paper and electronic copies of this gocument in whole iart.