Results 201 to 210 of about 18,237 (266)

An innovative fuzzy model control strategy for Trans-Z-Source DC-DC boost converters in satellite power systems. [PDF]

Sci Rep
Lotfy MW, Hussein AI, Abo-Zahhad MM, Mabrook MM.   +3 more
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A neuro-fuzzy multi-topology adaptive routing framework for QoS-aware healthcare IoT communications. [PDF]

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Parveen MS, Bhuvaneswari PTV.
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Enhancing MPPT performance of a grid-connected Doubly-Fed induction generator-based wind power plant using hybrid ANFIS-PI control strategy. [PDF]

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Biyazne LW, Tuka MB, Abebe YM, Wellhöfer A.   +3 more
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A drift-aware RS²FS pipeline with confidence gating for IDS. [PDF]

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Sakthivel G, Kumarasamy AK.
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Intelligent trajectory tracking in autonomous plantation robots using PSO-tuned nonlinear fuzzy PID, fuzzy PID, and conventional PID controllers. [PDF]

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Hailu GL, Yatayew TT.
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Multiobjective genetic fuzzy rule selection with fuzzy relational rules

2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), 2013
Genetic fuzzy rule selection has been frequently used for fuzzy rule-based classifier design. A number of its variants have also been proposed in the literature. In many studies on genetic fuzzy rule selection, each antecedent condition in fuzzy rules is given for a single input variable such as “x1 is small” and “x2 is large”.
Yusuke Nojima, Hisao Ishibuchi
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Towards Rule-ranking Based Fuzzy Rule Interpolation

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021
Recently proposed methods of TSK inference extension (TSK+) and K closest rules based TSK (KCR) are able to potentially perform fuzzy rule-based inference with a sparse rule base, for regression problems. However, in certain real-world applications, observations may be rather far away from any rules that may be fired in order to implement the required ...
Mou Zhou, Changjing Shang, Pu Zhang, Guobin Li, Shangzhu Jin, Jun Peng, Qiang Shen   +6 more
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Fuzzy rules generation from data through fuzzy evaluation of fuzzy rules

KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516), 2002
This paper deals with generating fuzzy rules from numerical data. The rule evaluation which is necessary in order to determine rule fitness with respect to given data is often based on mere heuristics and usually lacks in semantic interpretation. The method proposed in this paper is based on two central ideas which face these shortcomings.
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FUZZY RULES GENERATION FOR FUZZY CONTROL

Cybernetics and Systems, 1982
The paper deals with the design problem of control algorithms in fuzzy systems described by means of fuzzy relational equations, which can be implemented in the framework of fuzzy controllers applied to control of ill-defined systems. Some ways leading to assigning varying grades of importance to goals and constraints imposed in control process and ...
E. CZOGALA, W. PEDRYCZ
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