Particle Swarm Optimization with Turbulence (PSOT) is, in this paper, applied to find out fuzzy models to represent dynamic behavior of space systems that lie underneath the space qualification process. In optimizatio...
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Particle Swarm Optimization with Turbulence (PSOT) is, in this paper, applied to find out fuzzy models to represent dynamic behavior of space systems that lie underneath the space qualification process. In optimization area, each minimal improvement in results may represents a maximal, precious meaning and PSOT improve the performance of the established Particle Swarm Optimization (PSO) by introducing a slight variation, which simulates the action of an atmosphere turbulence to escape from local minima. This paper trades off the results of original PSO presented in a previous paper and PSOT both intertwined with Takagi-Sugeno (TS) fuzzy modeling dealing with experimental results of a thermal-vacuum system. Particle Swarm Optimization with turbulence has demonstrated to be a good alternative by taking into account the velocity of convergence to better solution and the total optimization time in generating dynamical models to the proposed system.
Goal driven intelligent agents and fuzzy reference gain-scheduling (FRGS) approach are described As interchangeable concepts that are able to deal with dynamic complex problems. It is advocated that the FRGS approach ...
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Goal driven intelligent agents and fuzzy reference gain-scheduling (FRGS) approach are described As interchangeable concepts that are able to deal with dynamic complex problems. It is advocated that the FRGS approach may be viewed as an autonomous goal-based agent, that is, a fuzzy logic based agent able to autonomously adapt itself to environmental changes introduced by external inputs. The concept of fuzzy systems and intelligent agent are employed in decision-making problems to lead to a certain degree of autonomy in decision support system. Although the FRGS method was originally proposed for control application, this approach was extended to decision-making tasks due to its ability of emulating human reasoning. This new agent approach uses the external input information also denominated reference (goal) as the driven mechanism to determine the behavior of the system in order to achieve the desired objectives (goal). Thus, the FRGS approach can be modeled in the framework of an adaptive and goal (also context or environment) driven agent.
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