FLIS

(redirected from Fuzzy Logic Inference System)
AcronymDefinition
FLISFederal Logistics Information System
FLISFuzzy Logic Inference System
FLISFlorida Lumber Inspection Service
FLISFiberized Link Interface Shelf (Nortel)
FLISFEMA (Federal Emergency Management Agency) Levee Inventory System (database)
FLISFuzzy Logic and Intelligent Systems (engineering and computing term)
References in periodicals archive ?
The fuzzy logic inference system quantified the input scenario to be assessed according to the rule base this study established, and yielded distinct quantitative output values after defuzzification.
The knowledge base characterizes the relationship between crisp input/output parameters and their fuzzy representation understood by the fuzzy logic inference system. Each input/output variable is characterized by the following items in the knowledge base:
The heart of a fuzzy logic inference system is composed of a set of IF-THEN rules used to determine the value of the output variables.
Let [THETA] = {c| [[mu].sub.c](c) > 0} denote a set of outputs c with membership value larger than zero, the appropriate crisp value at the output of the fuzzy logic inference system is derived as follows:
The measurement scale defined in fuzzy logic is a man-made fuzzy scale; for example, when the criterion of energy-saving family common understanding is energy-saving more than 30%, it means "Very good," 20% means "Good," 15% means "Good or Normal," 10% means "Normal," 5% means "Poor," and below 5% means "Very poor." The membership function in the fuzzy logic scale defines good or normal, and then the fuzzy logic inference system is used for defuzzification to complete the output results of quantized values.
Establish Fuzzy Logic Inference System. The establishment process of FLIS requires (1) inputting the selected criterion and the definition of fuzzy sets, (2) inputting the definition of the fuzzy sets of output values, (3) establishing the rule base of IF-THEN, (4) considering membership functions, and (5) obtaining the corresponding quantitative output value (figure or ratio) after FLIS defuzzification [21].
In order to control the elevation of the drone in flight, three fuzzy logic inference systems are designed.
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