Search results for "fuzzy inference"

showing 10 items of 35 documents

Finite-time stability and stabilisation for a class of nonlinear systems with time-varying delay

2014

This paper is concerned with the problems of finite-time stability FTS and finite-time stabilisation for a class of nonlinear systems with time-varying delay, which can be represented by Takagi–Sugeno fuzzy system. Some new delay-dependent FTS conditions are provided and applied to the design problem of finite-time fuzzy controllers. First, based on an integral inequality and a fuzzy Lyapunov–Krasovskii functional, a delay-dependent FTS criterion is proposed for open-loop fuzzy system by introducing some free fuzzy weighting matrices, which are less conservative than other existing ones. Then, the parallel distributed compensation controller is designed to ensure FTS of the time-delay fuzzy…

0209 industrial biotechnologyEngineeringfinite-time stabilisation; finite-time stability; fuzzy control; nonlinear system; time-delay system; Control and Systems Engineering; Theoretical Computer Science; Computer Science Applications1707 Computer Vision and Pattern RecognitionStability (learning theory)fuzzy controltime-delay system02 engineering and technologynonlinear systemFuzzy logicCompensation (engineering)Theoretical Computer Science020901 industrial engineering & automationControl theory0202 electrical engineering electronic engineering information engineeringfinite-time stabilisationfinite-time stabilityAdaptive neuro fuzzy inference systembusiness.industryComputer Science Applications1707 Computer Vision and Pattern RecognitionFuzzy control systemComputer Science ApplicationsWeightingNonlinear systemControl and Systems Engineering020201 artificial intelligence & image processingbusiness
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Optimal control of discrete-time interval type-2 fuzzy-model-based systems with D-stability constraint and control saturation

2016

This paper investigates the optimal control problem for discrete-time interval type-2 (IT2) fuzzy systems with pole constraints. An IT2 fuzzy controller is characterized by two predefined functions, and the membership functions and the premise rules of the IT2 fuzzy controller can be chosen freely. The pole assignment is considered, which is constrained in a presented disk region. Based on Lyapunov stability theory, sufficient conditions of asymptotic stability with an H ∞ performance are obtained for the discrete-time IT2 fuzzy model based (FMB) system. Based on the criterion, the desired IT2 state-feedback controller is designed to guarantee that the closed-loop system is asymptotically s…

0209 industrial biotechnologyMathematical optimizationAdaptive neuro fuzzy inference system02 engineering and technologyFuzzy control systemOptimal controlDefuzzificationFuzzy logic020901 industrial engineering & automationControl and Systems EngineeringControl theorySignal Processing0202 electrical engineering electronic engineering information engineeringFuzzy set operationsFuzzy number020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringSoftwareMathematicsSignal Processing
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Oxidative stability of virgin olive oil: evaluation and prediction with an adaptive neuro-fuzzy inference system (ANFIS).

2019

Background An adaptive neuro-fuzzy inference system (ANFIS) was employed to predict the oxidative stability of virgin olive oil (VOO) during storage as a function of time, storage temperature, total polyphenol, α-tocopherol, fatty acid profile, ultraviolet (UV) extinction coefficient (K268 ), and diacylglycerols (DAGs). Results The mean total quantities of polyphenols and DAGs were 1.1 and 1.9 times lower in VOOs stored at 25 °C than in the initial samples, and the mean total quantities of polyphenols and DAGs were 1.3 and 2.26 times lower in VOOs stored at 37 °C than in the initial samples, respectively. In a single sample, α-tocopherol was reduced by between 0.52 and 0.91 times during sto…

030309 nutrition & dieteticsInference systemalpha-TocopherolSingle sampleStability (probability)Diglycerides03 medical and health sciences0404 agricultural biotechnologyFood scienceOlive OilMathematics0303 health sciencesAdaptive neuro fuzzy inference systemNutrition and DieteticsQuality assessmentFatty AcidsTemperaturePolyphenols04 agricultural and veterinary sciencesModels Theoretical040401 food scienceFood StorageNonlinear modelAgronomy and Crop ScienceHybrid learning algorithmOxidation-ReductionFood ScienceBiotechnologyOlive oilJournal of the science of food and agricultureREFERENCES
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Inverse Kinematics for a 7 DOF Robotic Arm Using the Redundancy Circle and ANFIS Models

2014

In this paper we have presented a method to solve the inverse kinematics problem of a redundant robotic arm with seven degrees of freedom and a human like workspace based on mathematical equations, ANFIS implementation and Simulink models. For better visualization of the kinematics simulation a CAD model that mimics the real robotic arm was created into SolidWorks® and then the CAD parts were converted into SimMechanics model.

321 kinematic structureAdaptive neuro fuzzy inference systemEngineeringInverse kinematicsbusiness.industryCADGeneral MedicineKinematicsWorkspaceComputer Science::RoboticsControl theoryRedundancy (engineering)businessRobotic armAstrophysics::Galaxy AstrophysicsSimulationApplied Mechanics and Materials
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Fuzzy control of pH using NAL

1991

Abstract A fuzzy controller for a neutralization process is described. The controller was set up for a laboratory pilot plant. The approach is shown to be effective and can be extended to highly nonlinear and nonstationary processes. The “operator” knowledge encoded in the rules was obtained by several experimental runs of the system using manual control. Rules are composed using the max-min compositional rule of inference. The use of metarules, which depends on controller performance and on active disturbances, makes the controller behave like an adaptive controller. The control program is encoded in NAL, a new experimental logic programming language that was first used in this work in a r…

Adaptive neuro fuzzy inference systemAdaptive controlAutomatic controlComputer scienceApplied Mathematicsfuzzy logicpH controlexpert systemsFuzzy control systemprocess controladaptive controlDefuzzificationFuzzy logicTheoretical Computer Sciencelogic programmingArtificial IntelligenceControl theoryFuzzy numberSoftwareInternational Journal of Approximate Reasoning
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Fuzzy modeling and control for a class of inverted pendulum system

2014

Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/936868 Open Access Focusing on the issue of nonlinear stability control system about the single-stage inverted pendulum, the T-S fuzzy model is employed. Firstly, linear approximation method would be applied into fuzzy model for the single-stage inverted pendulum. At the same time, for some nonlinear terms which could not be dealt with via linear approximation method, this paper will adopt fan range method into fuzzy model. After the T-S fuzzy model, the PDC technology is utilized to design the fuzzy controller secondly. Numerical simulation res…

Adaptive neuro fuzzy inference systemArticle SubjectMathematics::General Mathematicslcsh:MathematicsApplied MathematicsFuzzy control systemAnalysis; Applied Mathematicslcsh:QA1-939VDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411Fuzzy logicInverted pendulumNonlinear systemControl theoryControl systemLinear approximationAnalysisMathematics
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Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series

2015

Abstract This paper makes a prediction of Chinese stock index (CSI) future prices using fuzzy sets and multivariate fuzzy time series method. We select Chinese CSI 300 index futures as the research object. The fuzzy time series model combines the fuzzy theory and the time series theory, thus this model can solve the fuzzy data in stock index futures prices. This paper establishes a multivariate model and improves the accuracy of computation. By combing traditional fuzzy time series models and rough set method, we use fuzzy c-mean algorithm to make the data into discrete. Further more, we deal with the rules in mature modules of the rough set and then refine the rules using data mining algor…

Adaptive neuro fuzzy inference systemComputer scienceCognitive NeuroscienceFuzzy setcomputer.software_genreStock market indexDefuzzificationFuzzy logicComputer Science ApplicationsArtificial IntelligenceFuzzy set operationsRough setData miningFutures contractcomputerNeurocomputing
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Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT

2018

This article presents the development of an intelligent technique of Adaptive-Neuro-Fuzzy Inference System (ANFIS) based on Maximum Power Point Tracking (ANFIS-MPPT) algorithm with PI controller in order to increase the performances of the photovoltaic panel system below change atmospheric circumstances. In this work, the mathematical principles of the ANFIS method were presented and developed using the software Matlab/Simulink. Moreover, the effectiveness of this ANFIS-MPPT technique is demonstrated by a comparison of the obtained results with others obtained from a classical (Perturb & Observe) P & O-MPPT method.From the analysis of the obtained results, the ANFIS-MPPT command provide bet…

Adaptive neuro fuzzy inference systemComputer sciencebusiness.industry020209 energyPhotovoltaic systemPID controller02 engineering and technologyMaximum power point trackingSoftwareControl theoryConvergence (routing)0202 electrical engineering electronic engineering information engineeringPoint (geometry)businessMATLABcomputercomputer.programming_language2018 7th International Conference on Renewable Energy Research and Applications (ICRERA)
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Data-based modeling of vehicle crash using adaptive neural-fuzzy inference system

2014

Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathematical point of view. In order to establish a mathematical model of a vehicle crash, one needs to consider various areas of research. For this reason, to simplify the analysis and improve the modeling process, in this paper, a novel adaptive neurofuzzy inference system (ANFIS-based) approach to reconstruct kinematics of colliding vehicles is presented. A typical five-layered ANFIS structure is trained to reproduce kinematics (acceleration, velocity, and displacement) of a vehicle involved in an oblique barrier collision. Subsequently, the same ANFIS structure is applied to simulate different…

Adaptive neuro fuzzy inference systemEngineeringVehicle crash reconstructionAdaptive neural-fuzzy inference system (ANFIS)-based prediction; Time-series analysis; Vehicle crash reconstruction; Vehicle dynamics modeling; Control and Systems Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic Engineeringbusiness.industryControl engineeringComputer Science Applications1707 Computer Vision and Pattern RecognitionKinematicsCollisionDisplacement (vector)Computer Science ApplicationsVehicle dynamicsAccelerationAdaptive neural-fuzzy inference system (ANFIS)-based predictionControl and Systems EngineeringTime-series analysisTime seriesElectrical and Electronic EngineeringbusinessReliability (statistics)Vehicle dynamics modeling
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Determining the Parameters of a Sugeno Fuzzy Controller Using a Parallel Genetic Algorithm

2013

Developed in the mid 1970s, the technique based on genetic algorithms proved its usefulness in finding optimal or near optimal solutions to problems for which accurate solving strategies are either non-existent or require excessively long running time. We implemented a genetic algorithm to determine the parameters of a Sugeno fuzzy controller for the Truck Backer-Upper problem (This problem is considered an acknowledged benchmark in nonlinear system identification.). Less known at first than Mamdami fuzzy controllers, Sugeno fuzzy controllers became popular once they were included into the ANFIS neuro-fuzzy Matlab library. By their nature, Sugeno controllers can be regarded as interpolation…

Adaptive neuro fuzzy inference systemMathematical optimizationFunction approximationControl theoryComputer scienceGenetic algorithmFuzzy setFuzzy control systemFuzzy logicInterpolation2013 19th International Conference on Control Systems and Computer Science
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