Search results for " Fuzzy Inference System"

showing 10 items of 28 documents

Inverse kinematics of a 7 DOF manipulator using Adaptive Neuro-Fuzzy Inference Systems

2012

This paper was carried out objectively to explore and describe the inverse kinematics solutions of an anthropomorphic redundant robotic structure with seven degrees of freedom and human like workspace. Traditional inverse kinematics methods can have an unacceptably slow pace for the today's extremely redundant systems. The presented method uses the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) editor and the Fuzzy Logic toolbox from MATLAB® which allow the investigation of various kinematical suitable solutions. ANFIS supports the determination of one degree of freedom, remaining therefore only six undetermined degrees. For better understanding of the simulations a CAD model that mimics th…

Adaptive neuro fuzzy inference systemNeuro-fuzzyInverse kinematicsControl theoryComputer scienceDegrees of freedom (statistics)Control engineeringCADWorkspaceFuzzy logicRobotic arm2012 12th International Conference on Control Automation Robotics & Vision (ICARCV)
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Adaptive neuro-fuzzy inference system for kinematics solutions of redundant robots

2016

This written paper presents aspects concerning the implementation of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the resolution of a redundant serial robot kinematics. The kinematics solutions are divided into two categories: direct kinematics solutions and inverse kinematics solutions. To be able to control a robot the most important solutions are the ones for the inverse kinematics since one knows the position and the final orientation of the end effector and needs to determine the relative displacement or movements into the robot couplings. To obtain the optimal solutions for the inverse kinematics of a redundant robot the mathematical equations were based onto the redundancy ci…

Adaptive neuro fuzzy inference systemRobot kinematicsEngineeringInverse kinematicsbusiness.industryKinematicsRobot end effectorlaw.inventionRobot controlComputer Science::RoboticslawKinematics equationsControl theoryRobotbusinessComputingMethodologies_COMPUTERGRAPHICS2016 6th International Conference on Computers Communications and Control (ICCCC)
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Type-2 Fuzzy Control of a Bioreactor

2009

Abstract—In this paper the control of a bioprocess using an adaptive type-2 fuzzy logic controller is proposed. The process is concerned with the aerobic alcoholic fermentation for the growth of Saccharomyces Cerevisiae a n d i s characterized by nonlinearity and parameter uncertainty. Three type-2 fuzzy controllers heve been developed and tested by simulation: a simple type-2 fuzzy logic controller with 49 rules; a type-2 fuzzyneuro- predictive controller (T2FNPC); a t y p e -2 selftuning fuzzy controller ( T2STFC). The T2FNPC combines the capability of the type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a…

Adaptive neuro fuzzy inference systemSettore ING-IND/26 - Teoria Dello Sviluppo Dei Processi ChimiciAdaptive controlArtificial neural networkNeuro-fuzzyComputer scienceFuzzy setFuzzy control systemEthanol fermentationFuzzy logicDefuzzificationNonlinear systemModel predictive controlControl theoryAdaptive systemAdaptive control Type-2 fuzzy control Non-linear systems UncertaintyProcess controlRobust control
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An FPGA-Based Adaptive Fuzzy Coprocessor

2005

The architecture of a general purpose fuzzy logic coprocessor and its implementation on an FPGA based System on Chip is described. Thanks to its ability to support a fast dynamic reconfiguration of all its parameters, it is suitable for implementing adaptive fuzzy logic algorithms, or for the execution of different fuzzy algorithms in a time sharing fashion. The high throughput obtained using a pipelined structure and the efficient data organization allows significant increase of the computational capabilities strongly desired in applications with hard real-time constraints.

Adaptive neuro fuzzy inference systemfuzzy inferenceCoprocessorAdaptive algorithmbusiness.industryComputer scienceMembership functionsControl reconfigurationSettore ING-INF/01 - ElettronicaFuzzy logicFuzzy logicFuzzy electronicsComputer Science::Hardware ArchitectureEmbedded systembusinessThroughput (business)Membership function
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Broken rotor bars detection via Park's vector approach based on ANFIS

2014

Many attempts have been made on fault diagnosis of induction motors based on frequency and time domain analysis of stator current. In this paper, first the Park's vector transformation and frequency analysis for fault detection of induction motors are introduced. Then a smart approach using Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed. This approach uses the time domain features derived from the Park's vector transformation of stator current. By the proposed method, a partial break including 5 mm crack on a bar, one broken bar and two broken bars using experimental data are investigated. It will be shown that features derived from Park's vector compared to features obtained fro…

EngineeringAdaptive neuro fuzzy inference systemRotor (electric)business.industryStatorANFIS; broken rotor bars; fault diagnosis; Park's transformation; Electrical and Electronic Engineering; Control and Systems EngineeringCoordinate vectorfault diagnosisFault (power engineering)Fault detection and isolationlaw.inventionlawControl theoryControl and Systems EngineeringTime domainElectrical and Electronic EngineeringbusinessANFISbroken rotor barsPark's transformationInduction motor
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Adaptive neural-fuzzy inference system based method to modeling of vehicle crash

2013

Various areas of research need to be considered in order to establish a mathematical model of a vehicle crash. To enhance the modeling process, a novel ANFIS-based approach to reconstruct behavior of impacting vehicles is presented in this paper. Kinematics of center of gravity (COG) a vehicle involved in an oblique barrier collision is reproduced by application of a five-layered ANFIS structure. Then, the same ANFIS system is used to simulate a different collision type than the one which was used in the training stage. The points of interests are selected to be the locations of accelerometers mounting. The accuracy of the proposed method is evaluated by the comparative analysis with the re…

EngineeringCenter of gravityAdaptive neuro fuzzy inference systembusiness.industryProcess (computing)Oblique caseControl engineeringStage (hydrology)KinematicsbusinessCollisionAccelerometerSimulation2013 IEEE International Conference on Mechatronics (ICM)
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A fuzzy logic approach to modeling a vehicle crash test

2013

Published version of an article in the journal: Central European Journal of Engineering. Also available from the publisher at: http://dx.doi.org/10.2478/s13531-012-0032-2 This paper presents an application of fuzzy approach to vehicle crash modeling. A typical vehicle to pole collision is described and kinematics of a car involved in this type of crash event is thoroughly characterized. The basics of fuzzy set theory and modeling principles based on fuzzy logic approach are presented. In particular, exceptional attention is paid to explain the methodology of creation of a fuzzy model of a vehicle collision. Furthermore, the simulation results are presented and compared to the original vehic…

EngineeringEnvironmental Engineeringmedia_common.quotation_subjectFuzzy setAerospace EngineeringFidelityCrashKinematicsFuzzy logicGeneral Materials Sciencevehicle crashElectrical and Electronic EngineeringCivil and Structural Engineeringmedia_commonAdaptive neuro fuzzy inference systemEvent (computing)business.industryMechanical EngineeringVDP::Technology: 500::Mechanical engineering: 570modelingControl engineeringEngineering (General). Civil engineering (General)Collisionfuzzy logicTA1-2040businessOpen Engineering
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A genetic integrated fuzzy classifier

2005

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.

Fuzzy classificationNeuro-fuzzyComputer scienceFuzzy setMachine learningcomputer.software_genreClassification Classifier Ensemble Evolutionary Algorithms.Artificial IntelligenceRobustness (computer science)Genetic algorithmCluster analysisAdaptive neuro fuzzy inference systemLearning classifier systemSettore INF/01 - InformaticaArtificial neural networkStructured support vector machinebusiness.industryPattern recognitionQuadratic classifierSupport vector machineComputingMethodologies_PATTERNRECOGNITIONSignal ProcessingMargin classifierFuzzy set operationsComputer Vision and Pattern RecognitionArtificial intelligencebusinesscomputerClassifier (UML)SoftwarePattern Recognition Letters
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A new fuzzy robust dynamic controller for autonomous vehicles with nonholonomic constraints

2005

Abstract In this paper a novel algorithm with a dynamic fuzzy controller applied to the control of trajectory of vehicles with two independent wheels is proposed. An automatic control of trajectory of a vehicle can behave in a not efficient way. It is necessary to consider the friction of the actuators and possible perturbations coming from the outside environment, as for instance the variable characteristics of the ground where the vehicle moves. These perturbations, which depend also on the contact between the wheel and the ground, involve violations of nonholonomic constraints. Thus it is necessary to compensate for these perturbations to obtain a robust control system. The controller sy…

Lyapunov functionMathematical optimizationAdaptive controlAutomatic controlComputer scienceGeneral MathematicsFuzzy logicComputer Science::Roboticssymbols.namesakeExponential stabilityControl theoryNonholonomic systemAdaptive neuro fuzzy inference systembackstepping controlautonomous vehicleFuzzy control systemComputer Science Applicationsnonholonomic systemFuzzy controllerControl and Systems EngineeringBacksteppingsymbolsTrajectoryRobust controlActuatorrobust controlSoftwareRobotics and Autonomous Systems
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Control of uncertain highly nonlinear biological process based on Takagi–Sugeno fuzzy models

2015

This note deals with the control of uncertain highly nonlinear biological processes. Indeed, an adaptive fuzzy control (AFC) scheme is developed for the pre-treatment of wastewater represented by a Takagi-Sugeno (TS) fuzzy model. The proposed approach uses a fuzzy system to approximate the unknown substrate consumption rate in designing the adaptive controller, and then an observer is designed to estimate the concentration in substrate at the outlet bioreactor. The observer is employed to generate an error signal for the adaptive control law which permits to minimize the influence of the measurement noise on the estimation of the substrate concentration. An update of the fuzzy models parame…

Lyapunov functionMathematical optimizationAdaptive neuro fuzzy inference systemEngineeringAdaptive controlObserver (quantum physics)business.industryFuzzy control systemFuzzy logicNonlinear systemsymbols.namesakeControl and Systems EngineeringControl theorySignal ProcessingsymbolsComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringbusinessSoftwareSignal Processing
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