In fuzzy systems, membershipfunctions determine the groups to which a variable can belong to, and these groups are static or only have one setting in some aspect. However, fuzzy systems typically require to model the...
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In fuzzy systems, membershipfunctions determine the groups to which a variable can belong to, and these groups are static or only have one setting in some aspect. However, fuzzy systems typically require to model the dynamic environment they represent. Still, this behavior does not reflect the membership groups in a conventional way. Thus, conventional fuzzy systems are not capable of reflecting the dynamics of the real-time context. The approach presented consists of a fuzzy system where the membershipfunctions can have dynamic transformations, according to contextual variables that influence them, to have a model that adjusts in real time. The membershipfunctions' dynamism is achieved because the form in the sets can be transformed;the maximum degree of membership of a set is in a range of zero to one;and, the location of the sets in the discourse universe can vary dynamically. The results show the feasibility of a context-based fuzzy system with dynamic membership functions built-in real time, that has been influenced by contextual variables. Therefore, unlike other proposals, this approach allows modeling the influence of the context on a fuzzy system, making it more adjusted to reality. To illustrate our proposed approach, a case study is presented where a fuzzy system estimates the heat stress in a work environment that uses data acquired from wearable devices. This system automatically generates the following indicators: (i) energy level wasted while performing a physical activity, (ii) personalized measurement of workload level, and (iii) measurement of Occupational Heat Stress (OHS).
Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error vari...
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Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation. The initial limits of the fuzzy logic membershipfunctions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membershipfunctions online using the k(th) and k - 1(th) outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membershipfunctions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems.
Contemporaneous MCDM methodology is based on the simplifying assumption of independent objectives. This restriction was partly relaxed through the concept of(static) interdependent objectives introduced by C. Carlsson...
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Contemporaneous MCDM methodology is based on the simplifying assumption of independent objectives. This restriction was partly relaxed through the concept of(static) interdependent objectives introduced by C. Carlsson and R. Fuller. Most real world managerial decision problems involve interdependent objectives, yet in a temporal setting. In the paper we generalize the static concept to temporal fuzzy multiobjective programming problem. We introduce the concepts temporal support and temporal conflict in the objective set within both infinite and finite planning horizons. We also formulate dynamic versions of membershipfunctions for interdependent objectives in crisp and fuzzy multiobjective programming problems. The new concepts are used to describe and model temporal goal conflicts in numerical illustrations. (C) 1997 Elsevier Science B.V.
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