Quantum-behaved particle swarm optimization (qpso)is an algodthm,which has good global optimization effects,and simple *** it has inherent *** Programmable Gate Array (FPGA)with a fine-grained parallel computing capab...
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Quantum-behaved particle swarm optimization (qpso)is an algodthm,which has good global optimization effects,and simple *** it has inherent *** Programmable Gate Array (FPGA)with a fine-grained parallel computing capabilities,is suitable as qpso high computing *** paper designed a high-performance computing platform for qpso,and realized in XILINX company's SPARTAN-Ⅲ*** whole design structure of the system is modular,and use pipeline technology to optimize the *** testing some reliable benchmark functions,the computing platform that can be effective in reducing the running time and improve qpso practical value.
The primary objective of this study was to optimize the trajectory planning and positioning error of a five-degree-of-freedom robotic manipulator. First, a multigroup ant colony optimization (MACO) algorithm was used ...
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The primary objective of this study was to optimize the trajectory planning and positioning error of a five-degree-of-freedom robotic manipulator. First, a multigroup ant colony optimization (MACO) algorithm was used to determine the shortest trajectory with obstacle avoidance. A quantum-behaved particle swarm optimization (qpso) algorithm was subsequently used to determine the optimal positioning error for each moving point along the shortest trajectory. The simulation results revealed that compared with the traditional ant colony optimization algorithm, the MACO algorithm reduced the number of iterations by 66% and the path length by approximately 5%-7%. Moreover, the qpso algorithm ensured that the robotic manipulator performed the least possible number of joint movements. This algorithm also solved the inverse kinematics problem. The optimal configuration had a positioning error of 10 mm. The current results can be extensively used for designing and analyzing multiaxial robotic manipulators.
A technique for Fuzzy Cognitive Maps learning,which is based on the Quantum-behaved Particle Swarm Optimization algorithm,is *** proposed approach is used for updating the nonzero weight values that lead the Fuzzy Cog...
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A technique for Fuzzy Cognitive Maps learning,which is based on the Quantum-behaved Particle Swarm Optimization algorithm,is *** proposed approach is used for updating the nonzero weight values that lead the Fuzzy Cognitive Map to desired steady *** workings of the approach are applied to an industrial control *** results support the claim that the proposed technique is a promising methodology for Fuzzy Cognitive Maps learning,and the methodology is effective and efficient.
On the problem of Global maximum power point tracking（GMPPT） caused by partially shaded in photovoltaic system, many scholars’ search methods are always judged by their ability of static search, and dynamic perform...
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On the problem of Global maximum power point tracking（GMPPT） caused by partially shaded in photovoltaic system, many scholars’ search methods are always judged by their ability of static search, and dynamic performances when irradiance suddenly changesBut these methods neglect the dynamic performance when irradiance changes frequentlyThis article proceeded from the changes of maximum power point, which are according to the local approximate output characteristic in the process of illumination changes, analyzed the reason of the dead zone caused by the conventional optimization algorithms and combined optimization algorithm, and illustrated the necessity of the global optimization in the whole processThen, according to the request of the whole and global optimization strategy, this article proposed a Quantum-behaved Particle Swarm Optimization（qpso） to increase the diversity of the particles, the search velocity and convergence precisionFinally, the method was simulated by Maltab/SimS cape and compared with the standard particle swarm optimization algorithm, showing the superiority of the algorithm in solving the problem of GMPPT caused by partially shaded.
Infrared and visible dual camera is commonly used in UAV inspection. As a crucial preprocess, accurate registration can promote subsequent multi-source image fusion, which can improve detectability and reduce the fals...
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ISBN:
(纸本)9781450397568
Infrared and visible dual camera is commonly used in UAV inspection. As a crucial preprocess, accurate registration can promote subsequent multi-source image fusion, which can improve detectability and reduce the false positive rate. Due to significant geometric distortions, gray-scale differences and partial overlap between infrared image and visible image, feature-based methods or joint area-feature based methods cannot obtain satisfactory results. To solve this problem, this paper presents a novel registration method based on UAV imaging characteristics and intensity-structure similarity optimization. The reliable initial registration parameters are obtained by utilizing the UAV imaging parameters and approximate coaxial imaging principle. For further improving the accuracy of registration, this paper proposes an intensity-structure similarity metric and the final rectification parameters are obtained by maximizing the proposed metric via quantum particle swarm optimization (qpso) method. The experimental results of infrared and visible images in UAV inspection of photovoltaic power station demonstrate that the proposed method is competitive against traditional feature-based methods (FBMs), such as SIFT and SURF, and the joint area-feature based methods (AFBMs) based on SIFT combined with regional mutual information.
This paper proposes an image fusion approach based on qpso *** formulate the image fusion problem as an optimization problem and adopt Quantum-behaved Particle Swarm Optimization algorithm to solve the problem. Not on...
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This paper proposes an image fusion approach based on qpso *** formulate the image fusion problem as an optimization problem and adopt Quantum-behaved Particle Swarm Optimization algorithm to solve the problem. Not only qpso has less parameter to control,but also does its sampling space at each iteration cover the whole solution *** qpso can find the best solution quickly and guarantee to be global *** this paper,another two methods,Genetic algorithm(GA) and Particle Swarm Optimization(PSO) are tested for performance comparison with qpso,and the result show the good efficiency of qpso algorithms to image fusion.
A fault tolerant control method is proposed in this paper for a turbofan engine gas path degradation through the full flight envelope. A Quantum-behaved Particle Swarm Optimization(qpso) algorithm is applied to obtain...
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A fault tolerant control method is proposed in this paper for a turbofan engine gas path degradation through the full flight envelope. A Quantum-behaved Particle Swarm Optimization(qpso) algorithm is applied to obtain engine inputs adjustments, which contribute to construct off-line performance accommodation interpolation schedules. With a double closed-loop control system structure, command control is corrected based on real-time fault diagnostic results. Simulations indicate that fault tolerant control could reduce thrust and stall margin loss effectively in gas path faults.
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