Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, networ...Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.展开更多
To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totali...To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory： information system discrete algorithm （algorithm 1） and samples representatives judging algorithm （algorithm 2）. On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.展开更多
Irrigation water became the limiting factor to the persistent improvement of grain production. Based on the data from Gannan County, a semiarid area in the west of Heilongjiang Province, the present situation of the d...Irrigation water became the limiting factor to the persistent improvement of grain production. Based on the data from Gannan County, a semiarid area in the west of Heilongjiang Province, the present situation of the development and utilization of water resources and the suitable water saving irrigation mode were analyzed by using SPA model, which was significant to the efficient and rational utilization of water resources and the improvement of agriculture productivity. The result showed that the model could be applied well to the assessment of development and utilization of water resources and the multi-project optimal selection. Through calculation, it could be found that the utilization of water resources in Gannan County was still in the primary stage, and the integration technology of the optimized water saving irrigation should be combined to support the sustainable development of agriculture in the semiarid area.展开更多
基金Projects(70373017 70572090) supported by the National Natural Science Foundation of China
文摘Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.
文摘To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory： information system discrete algorithm （algorithm 1） and samples representatives judging algorithm （algorithm 2）. On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.
基金Supported by Heilongjiang Province Technological and Scientific Research Project(12531051)
文摘Irrigation water became the limiting factor to the persistent improvement of grain production. Based on the data from Gannan County, a semiarid area in the west of Heilongjiang Province, the present situation of the development and utilization of water resources and the suitable water saving irrigation mode were analyzed by using SPA model, which was significant to the efficient and rational utilization of water resources and the improvement of agriculture productivity. The result showed that the model could be applied well to the assessment of development and utilization of water resources and the multi-project optimal selection. Through calculation, it could be found that the utilization of water resources in Gannan County was still in the primary stage, and the integration technology of the optimized water saving irrigation should be combined to support the sustainable development of agriculture in the semiarid area.
