Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems

Zadeh, Lotfi A.; Klir, George J.; Yuan, Bo

doi:10.1142/2895

Cited by 582 publications

(297 citation statements)

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“…Fuzzy logic is an approach founded on "degrees of truth" rather than the usual "true or false" values (1 or 0). The idea of fuzzy logic was first proposed by Zadeh in the 1960s when he was working on the problem of computer understanding of natural language [47]. Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory [3] to deal with approximate reasoning.…”

Section: Fuzzy Logic As Agents' Knowledge Representationmentioning

confidence: 99%

Fuzzy logic in the multi-agent financial decision support system

Korczak

Hernes

Bac

2015

Annals of Computer Science and Information Systems

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Abstract-The article presents the application of a fuzzy logic in building the trading agents of the a-Trader system. The system supports investment decisions on the FOREX market. The first part of the article contains a discussion related to the use of fuzzy logic as an agents' knowledge representation. Next, the algorithms of the selected fuzzy logic buy-sell decision agents are presented. In the last part of the article the agent performance is evaluated on real FOREX data.

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Section: Fuzzy Logic As Agents' Knowledge Representationmentioning

confidence: 99%

Fuzzy logic in the multi-agent financial decision support system

Korczak

Hernes

Bac

2015

Annals of Computer Science and Information Systems

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“…FST has a long history of effective use in engineering applications, particularly control system dynamics [10,11,12]. More recently, it has been applied to a variety of systems that share certain characteristics, such as Geographic Information System algorithms for spatial decision modeling, ecosystem modeling [13,14,15,16,17] and so forth.…”

Section: Principles Of Casewise Visual Evaluation (Cave)mentioning

confidence: 99%

“…Set operations and data computations are governed by set theory identically to crisp sets. However, output takes the form of a probability function rather than a singular certainty [21,10,12]. The CAVE modeling process entails a transformation from numerical value to categorical value during fuzzy set input and PKB build and then from categorical value to numerical value when the PKB is queried.…”

Section: Principles Of Casewise Visual Evaluation (Cave)mentioning

confidence: 99%

Community design of a light rail transit-oriented development using casewise visual evaluation (CAVE)

Bailey

Grossardt

Pride-Wells

2007

Socio-Economic Planning Sciences

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“…These methods include fuzzy logic (FL), neural networks (NN), genetic algorithms (GA), and probabilistic reasoning (PR). In addition, these methodologies in most part are complimentary rather than competitive [9].…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Bazazzadeh

Badihi

Shahriari

2011

International Journal of Aerospace Engineering

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This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC) for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc.) are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.

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Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems

Cited by 582 publications

References 0 publications

Fuzzy logic in the multi-agent financial decision support system

Fuzzy logic in the multi-agent financial decision support system

Community design of a light rail transit-oriented development using casewise visual evaluation (CAVE)

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

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