Soft computing, fuzzy logic, and artificial intelligence

Dubois, Didier; Prade, Henri

doi:10.1007/s005000050025

Cited by 22 publications

(15 citation statements)

References 18 publications

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“…Fuzzy logic models the human capability of computing with words: it is a rigorous logic of imprecise and vague reasoning. It plays an important role in the development of artificial intelligence, [16][17][18][19] and it is also processed by molecules. [20][21][22] Fuzzy inference systems with adaptive capabilities are drawing increasing attention, since they can model the human ability to make decisions in complex situations and fine-tune the membership functions according to a desired input-output data set of any nonlinear function.…”

Section: Predictive Methodsmentioning

confidence: 99%

Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor

Gentili

Gotoda

Dolnik

et al. 2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Forecasting of aperiodic time series is a compelling challenge for science. In this work, we analyze aperiodic spectrophotometric data, proportional to the concentrations of two forms of a thermoreversible photochromic spiro-oxazine, that are generated when a cuvette containing a solution of the spiro-oxazine undergoes photoreaction and convection due to localized ultraviolet illumination. We construct the phase space for the system using Takens' theorem and we calculate the Lyapunov exponents and the correlation dimensions to ascertain the chaotic character of the time series. Finally, we predict the time series using three distinct methods: a feed-forward neural network, fuzzy logic, and a local nonlinear predictor. We compare the performances of these three methods.

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Section: Predictive Methodsmentioning

confidence: 99%

Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor

Gentili

Gotoda

Dolnik

et al. 2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…(McCarthy, 2007). More recently the field of Soft Computing has emerged, relating to the merging of a variety of approaches including Artificial Neural Networks, Fuzzy Logic and Evolutionary Algorithms (Dubois and Prade, 1998) and can be defined as "computational methods tolerant to sub optimality and impreciseness (vagueness) and giving quick, simple and sufficiently good solutions" (Zadeh, 1994). An overview of applications of AI in the environmental sciences can be found in Haupt et al (2009).…”

Section: Artificial Intelligence Approachesmentioning

confidence: 99%

Laser scanning and digital outcrop geology in the petroleum industry: A review

Hodgetts

2013

Marine and Petroleum Geology

175

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“…This trend gets away from the original motivations of fuzzy logic. 24 It seems to be caused by the formal analogy between the mathematical models of fuzzy rules and artificial neurons, and the popularity of the "soft computing" paradigm. However, restricting fuzzy rules to such a role puts them in direct competition with many other and older methods for approximating functions.…”

Section: Introductionmentioning

confidence: 99%

A new perspective on reasoning with fuzzy rules

Dubois

Prade

Ughetto

2003

Int. J. Intell. Syst.

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This article expresses the idea that information encoded on a computer may have a negative or positive emphasis. Negative information corresponds to the statement that some situations are impossible. Often, it is the case for pieces of background knowledge expressed in a logical format. Positive information corresponds to observed cases. It is encountered often in data-driven mathematical models, learning, etc. The notion of an "if . . . , then . . ." rule is examined in the context of positive and negative information. It is shown that it leads to the three-valued representation of a rule, after De Finetti, according to which a given state of the world is an example of the rule, a counterexample to the rule, or is irrelevant for the rule. This view also sheds light on the typology of fuzzy rules. It explains the difference between a fuzzy rule modeled by a many-valued implication and expressing negative information and a fuzzy rule modeled by a conjunction (a la Mamdani) and expressing positive information. A new compositional rule of inference adapted to conjunctive rules, specific to positive information, is proposed. Consequences of this framework on interpolation between sparse rules are also presented.

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Soft computing, fuzzy logic, and artificial intelligence

Abstract: 1Other components are advocated as being part of soft computing: probabilistic networks, rough sets theory, and chaos theory especially. This paper focuses on the three main components.

Cited by 22 publications

References 18 publications

Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor

Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor

Laser scanning and digital outcrop geology in the petroleum industry: A review

A new perspective on reasoning with fuzzy rules

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