Dissolved gas analysis is an effective method for the early detection of incipient fault in power transformers. To improve the capability of interpreting the result of dissolved gas analysis, a technology is propo...
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ISBN:
(纸本)9781424479573
Dissolved gas analysis is an effective method for the early detection of incipient fault in power transformers. To improve the capability of interpreting the result of dissolved gas analysis, a technology is proposed in this paper. The particleswarmoptimization (PSO) technique is used to integrate with Back Propagation (BP) neural networks, and using particleswarm to optimize the network's weights and biases, the fault of transformers is simulated and discussed. The results show that the accuracy of PSO-BP method is significantly higher than that of the conventional three-ratio method. So the algorithm based on PSO-BP network model provides a more accurate, safe and reliable result for the fault diagnosis of transformers.
In order to solve the problem of linearization,complexity and poor accuracy for parameter estimate of Muskingum Routing Model at present,this paper introduces three modern intelligent algorithms-Genetic algorithm(GA),...
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In order to solve the problem of linearization,complexity and poor accuracy for parameter estimate of Muskingum Routing Model at present,this paper introduces three modern intelligent algorithms-Genetic algorithm(GA),Simulated Annealing algorithm(SA) and particle swarm optimization algorithm(PSO) for the parameter calibration of Muskingum *** specific simulation,the results of five methods are *** according to the calculation,comparison and analysis of five methods comprehensively,it is found that the results of three modern intelligent algorithms are fit significantly and better than traditional methods.
The flow shop scheduling problem (FSSP) is a NPHARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the m...
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The flow shop scheduling problem (FSSP) is a NPHARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the major evolution direction, the effectiveness of regular genetic algorithm is restricted. In this paper, the particleswarmoptimizationalgorithm (PSO) is introduced for better initial group. By combining PSO with GA, a hybrid optimizationalgorithm for FSSP is proposed. This method is validated on a series of benchmark datasets. Experimental results indicate that this method is efficient and competitive compared to some existing methods.
An improved particleswarmoptimization (IPSO) algorithm is proposed to solve a typical combinatorial optimization problem: traveling salesman problem, which is a well-known NP-complete problem. In the improved algori...
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ISBN:
(纸本)9812565329
An improved particleswarmoptimization (IPSO) algorithm is proposed to solve a typical combinatorial optimization problem: traveling salesman problem, which is a well-known NP-complete problem. In the improved algorithm, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other individuals according to certain probability. This kind of study behavior accords with the biological natural law even more, and furthermore helps to find the global optimum solution. At the same time, this paper proposes the concepts of Adjustment Operator and Adjustment Sequence based on which particleswarmoptimization (PSO) and IPSO algorithm were successfully rebuilt, according to the ideas of single node regulating algorithm. For solving traveling salesman problem, numerical simulation results show the effectiveness and efficiency of the proposed method.
Two off-line neural networks were trained by applying particle swarm optimization algorithm to create object model and object inverse model of model reference adaptive inverse control system. The method and procedure ...
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ISBN:
(纸本)078039044X
Two off-line neural networks were trained by applying particle swarm optimization algorithm to create object model and object inverse model of model reference adaptive inverse control system. The method and procedure in training the network of control system was given by using particleswarm. Double inverted pendulum system was used for research object in simulation. The result of experiment proved that this algorithm can obtain more stability performance, and easy to achieve.
Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. ...
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Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. particleswarmoptimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.
The performance of a fragile watermarking method based on discrete cosine transform (DCT) has been improved in this paper by using intelligent optimizationalgorithms (IOA). namely genetic algorithm, differential evol...
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The performance of a fragile watermarking method based on discrete cosine transform (DCT) has been improved in this paper by using intelligent optimizationalgorithms (IOA). namely genetic algorithm, differential evolution algorithm, clonal selection algorithm and particle swarm optimization algorithm. In DCT based fragile watermarking techniques, watermark embedding can usually be achieved by modifying the least significant bits of the transformation coefficients. After the embedding process is completed, transforming the modified coefficients from the frequency domain to the spatial domain produces some rounding errors due to the conversion of real numbers to integers. The rounding errors caused by this transformation process were corrected by the use of intelligent optimizationalgorithms mentioned above. This paper gives experimental results which show the feasibility of using these optimizationalgorithms for the fragile watermarking and demonstrate the accuracy of these methods. The performance comparison of the algorithms was also realized. (C) 2009 Elsevier B.V. All rights reserved.
Magnetic flux leakage (MFL) testing is widely used to examine ferromagnetic materials. For the reason of estimating the sizes of cracks in metals is important in piping industries, a fast method based on particle swar...
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Magnetic flux leakage (MFL) testing is widely used to examine ferromagnetic materials. For the reason of estimating the sizes of cracks in metals is important in piping industries, a fast method based on particle swarm optimization algorithm is proposed for reconstructing the sizes of rectangular crack in this article. Considering the magnetic leakage field intensity is related to the air gap between the inspection specimen and the sensor, we give the reconstruction results in different lift-off values. Besides, the influence of different magnetic conditions to the reconstruction effectiveness has been investigated. The simulation results have shown the rapidity and accuracy of the proposed method. (C) 2009 Elsevier Ltd. All rights reserved.
Two sub-swarms substituting particle swarm optimization algorithm (TSSPSO) is proposed. The algorithm parameters are analyzed and the iteration equations are amended. The new algorithm assumes that particles are divid...
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Two sub-swarms substituting particle swarm optimization algorithm (TSSPSO) is proposed. The algorithm parameters are analyzed and the iteration equations are amended. The new algorithm assumes that particles are divided into two sub-swarms. One sub-swarm flies toward the global best particle, and the other flies in the opposite direction. Not only its search experience and the best individual's position of its own sub-swarm, but also the best individual's position of the whole swarm can affect each particle's search during iterations. Each iteration, some bad particles of one sub-swarm are replaced with some good particles of another under a substituting probability. Then, both TSSPSO and particle swarm optimization algorithm (PSO) are used to resolve four well-known and widely used test functions' optimization problems. Results show that TSSPSO has greater efficiency, better performance and more advantages than PSO in many aspects. In addition, TSSPSO is applied to train artificial neural network to construct a practical soft-sensor of gasoline endpoint of crude distillation unit. The obtained results and comparison with actual industrial data indicate that the new method is feasible and effective in soft-sensor of gasoline endpoint.
Aiming at the demerits of extremum random disturbed arithmetic operator of a particle swarm optimization algorithm, the reasonable amelioration is put forward based on the design idea of extremum random disturbed arit...
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ISBN:
(纸本)9780769537450
Aiming at the demerits of extremum random disturbed arithmetic operator of a particle swarm optimization algorithm, the reasonable amelioration is put forward based on the design idea of extremum random disturbed arithmetic operator. An improved particle swarm optimization algorithm is put forward and applied to parameter selection of support vector machine. The regress modeling of two common functions based on least square support vector machine is to be as examples and the simulation experiment is done. The results show that the amelioration of arithmetic operator is necessary and feasible. The convergence velocity and precision of algorithm are enhanced.
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