Fuzzy Inference Systems
Tuning Up Fuzzy inference systems by using optimization algorithms for the classification of solar flares
... of fuzzy rules, a data base that defines the membership functions of the fuzzy sets used in fuzzy rules, the fuzzy inference engine, the fuzzifier and defuzzifier ...a fuzzy ...
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ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment
... (GAs), fuzzy logic (FL), adaptive neuro fuzzy inference systems (ANFIS), support vector machines (SVM) and data mining ...nonlinear systems, especially when the underlying physical ...
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A review of artificial intelligent approaches applied to part accuracy prediction
... the fuzzy inference systems are defined by the expert machinist in order to incorporate expert knowledge into the ...the fuzzy system. For this purpose, the fuzzy rules can be extracted ...
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Programming of Job Shop Production Systems with Fuzzy Logic
... Este artículo propone un algoritmo de toma de decisiones con base en la lógica difusa (fuzzy logic) como técnica de optimización, que permita encontrar una buena solución al problema de determinar la prioridad ...
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Un estudio comparativo entre ANFIS, ANNs y SONFIS para series temporales volátiles
... 27. Soto, J., Melin, P., Castillo, O.: Particle Swarm Optimization of the Fuzzy Integra- tors for Time Series Prediction Using Ensemble of IT2FNN Architectures, pp. 141– 158. Springer–Verlag (2017), ...
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Predicting the risk of fault-induced water inrush using the adaptive neuro-fuzzy inference system
... two fuzzy subsets included within the discourse domain: the subset of water inrush occurring (Equation (10)) and the subset of water inrush not occurring (Equation ...
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Some advances in constrained inference for ordered circular parameters in oscillatory systems
... statistical inference and at least four books on the subject ...statistical inference is gaining considerable interest among applied researchers in a variety of fields, such as toxicology (Peddada et ...
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Robot arm fuzzy control by a neuro-genetic algorithm
... the fuzzy membership functions for the output variables and the rule base construction is a more dicult ...seven fuzzy sets for each output variable, the deni- tion of the rules to consider which action ...
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Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems
... a fuzzy domain composed by fuzzy sets. These fuzzy sets have been obtained as a result of the analysis of the histograms associated with the distributions of values taken by each derived parameter ...
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A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
... randomicity, fuzzy and imperfection, being in existence in the process of fish-disease diagnosis, the degree of manifestations is sufficiently taken into account, a new method for fish-disease diagnostic problem ...
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PROPUESTAS PARA LA PREDICCIÓN DEL SOBREENDEUDAMIENTO EN HOGARES DE CHILE MEDIANTE EL USO DE UN MODELO HÍBRIDO QUE MEZCLA "ARTIFICIAL NEURO FUZZY INFERENCE SYSTEM" Y MODELO PROBIT
... El aumento en los niveles de deuda de las familias de distintas partes del mundo ha atraído la atención de organizaciones locales y mundiales dedicadas a la prevención de riesgos financieros, y ha intensificado el ...
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Informe d'Autors UOC a ISI Web of Knowledge Juliol 2014
... Rosas, C., Sikora, A., Jorba, J., Moreno, A., Espinosa, A., & Cesar, E. (2014). Dynamic tuning of the workload partition factor and the resource utilization in data-intensive applications. Future Generation Computer ...
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Fuzzy systems: case study classification of fruit Mc Stipitata Vaug (Áraza)
... I fuzzy logic block is used for the classification of the fruit Mc Stiptita Vaug depending on the color of the element, for the implementation of this type of block it is necessary to determine with precision the ...
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A view on Fuzzy Systems for big data: progress and opportunities
... Finally, there are plenty of new and complex paradigms that are growing in importance in the last years that must be also taken into account. We may refer to multi-instance learning 17 , 62 , 63 , multi- label 9 , 26 , ...
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Followee recommendation in twitter using fuzzy link prediction
... the fuzzy system is a strong com- petitor both inside and outside ...the fuzzy system’s accuracy lies behind for approximately 3%, but let us also note that the fuzzy system is only using three ...
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Robust Fuzzy Clustering via Trimming and Constraints
... Constraint on the scatter parameters: The constant c serves to control the degree of “heteroscedasticity” in the obtained clusters. A large c value al- lows for more different variances in the error terms when using ...
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Evolution of recurrent fuzzy controllers
... Figure 2: The diagram (a) shows the application area of a fuzzy rule and the diagram (b) shows the application area of a neighbor rule. The diagram (c) shows the application area of the first rule when the second ...
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A Fuzzy Logic Inference Approach for the Estimation of the Passengers Flow Demand
... of fuzzy logic has proven to be a promising tool because it can integrate the railway planning experts’ experience in multiple scenarios (Aldian et al, 2003) (Cheng et al, ...
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Probabilistic inference for dynamical systems
... of inference in dynamical systems, written in the language of Bayesian ...of inference over paths from which we obtain the continuity equation and Cauchy’s equation for fluid dynamics, and discuss ...
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An inference engine based on fuzzy logic for uncertain and imprecise expert reasoning
... Adictionary has been foreseen in every knowledge base to produce a user-friendly interface. It is, there- fore, possible to interact with the system using the usual linguistic values of the universe of discourse to which ...
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