Membership Functions for Fuzzy Focal Elements

Porębski, Sebastian; Straszecka, Ewa

doi:10.1515/acsc-2016-0022

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…The detailed shape of these functions depends on the parameters associated with the parameters and state variables of the ISD system. We recommend using the Gaussian or the generalized bell membership function (Porębski and Straszecka 2016). Another element of the discussed fuzzy model of the needs system is a need weighting function, depicted by a dashed line in Fig.…”

Section: Model Of Needsmentioning

confidence: 99%

Cognitive motivations and foundations for building intelligent decision-making systems

Kowalczuk

Czubenko

2022

Artif Intell Rev

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Concepts based on psychology fit well with current research trends related to robotics and artificial intelligence. Biology-inspired cognitive architectures are extremely useful in building agents and robots, and this is one of the most important challenges of modern science. Therefore, the widely viewed and far-reaching goal of systems research and engineering is virtual agents and autonomous robots that mimic human behavior in solving known and unknown problems. The article proposes, at a high level of generality, an operational cybernetic model of the human mind, developed with the use of carefully selected ideas taken from psychological knowledge. In particular, the work combines extensive knowledge drawn from both the theory of developmental cognitive psychology and the theory of motivation. The proposed mathematically developed operating blocks create a coherent and functional decision-making system containing all the elements necessary in autonomous robotics. The ISD system is under development. There is still a long way to go to full validation. However, as shown in several articles, the basic subsystems of the ISD system, i.e. motivational and emotional, have already been positively verified in operation. The overall purpose of this article is to show a blueprint of the overall concept of the entire ISD.

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Section: Model Of Needsmentioning

confidence: 99%

Cognitive motivations and foundations for building intelligent decision-making systems

Kowalczuk

Czubenko

2022

Artif Intell Rev

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“…To do so, eight different membership functions (MF), including, Triangular (trimf) [39], Trapezoidal (trapmf), Generalized bellshaped (gbellmf) [40], Gaussian (gaussmf), Gaussian combination (gauss2mf) [41], Pi-shaped (pimf) [42], Difference between two sigmoidal membership functions (dsigmf) [43], and Product of two sigmoidal membership (dsigmf) [44] , where σ and c are the Standard Deviation (SD) and mean parameters, respectively. The III.To assess the effectiveness of the parameter t sc on performance of the controller, two controllers were built with the same structure and membership functions; however, the controller was firstly built without inputting t sc and then t sc was fed to the controller.…”

Section: B Controller Designmentioning

confidence: 99%

Simultaneous Intelligent Anticipation and Control of Follower Vehicle Observing Exiting Lane Changer

Tajdari

Golgouneh

Ghaffari

et al. 2021

IEEE Trans. Veh. Technol.

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Despite the advances related to car-following and lane-changing behaviors, the influence of lane-changing on the car-following models, which results in a complex transient merging behavior, has not comprehensively been investigated. This paper presents a novel fuzzy controller based on a human factor to optimize the Follower Vehicle (FV) behavior subject to safety, comfort, and convenient traveled time in the complex behavior where the Lane Changer (LC) vehicle exits the temporary lane. The factor enables the controller to mimic the current driver behavior in terms of maximum pleasantness of drive. Accordingly, the data of real-life experiments were used to design the human-like fuzzy controller, to build a predictive model to suggest the appropriate acceleration, velocity, and travel distance. At best, the correlation coefficient of 0.93 and the Root Mean Square Error (RMSE) of 0.71 were achieved for modeling using the adaptive Neuro-Fuzzy Inference System (ANFIS) utilizing Gaussian function as a membership function. Furthermore, to evaluate the robustness of the controller to uncertainties and unknown disturbances for real-time driving experiments, a test-bed was fabricated to mount the feedback sensors, including vision, accelerometer, and distance measurement sensors. The results of running the controller in various driving scenarios showed 70% and 38% improvements in safety and ride comfort, respectively. The proposed intelligent controller is intended to be used for vehicle route guidance and on urban highways.

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“…where A l i is the system input language variables and B l i is the system output language variables. The specific fuzzy adjustment rules 28 are designed as follows:…”

Section: Representation Of the Fuzzy Rule Librarymentioning

confidence: 99%

A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator

Liang

Shi

Tian

2018

Advances in Mechanical Engineering

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A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator is proposed. Based on the Udwadia–Kalaba motion constraint equation, the dynamic equation of the constrained manipulator with both ideal and non-ideal constraints is obtained. Considering the uncertainty of the terminal non-ideal constrained force and the chattering phenomenon of sliding mode control, the adaptive fuzzy and the sliding mode control method are combined to control the constrained manipulator. Because the system is constrained, the model order reduction method is innovatively used in the control algorithm. The stability of the system is proved by Lyapunov theorem. For demonstrating the effectiveness of the control algorithm, the 2-degree-of-freedom manipulator is taken as the research object. Finally, the high-precision control of the manipulator is achieved and the chattering phenomenon caused by the sliding mode control is weakened.

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Membership Functions for Fuzzy Focal Elements

Cited by 8 publications

References 32 publications

Cognitive motivations and foundations for building intelligent decision-making systems

Cognitive motivations and foundations for building intelligent decision-making systems

Simultaneous Intelligent Anticipation and Control of Follower Vehicle Observing Exiting Lane Changer

A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator

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