By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant ...By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.展开更多
In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath f...In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.展开更多
A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover opera...A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover operator, mutation operators and adaptive probabilities for these operators. The algorithm is tested by two generally used functions and is used in training a neural network for image recognition. Experimental results show that the algorithm is an efficient global optimization algorithm.展开更多
An experimental platform accompanying with the improved Roberts algorithm has been developed to achieve accurate and real-time edge detection of surface defects on heavy rails.Detection results of scratching defects s...An experimental platform accompanying with the improved Roberts algorithm has been developed to achieve accurate and real-time edge detection of surface defects on heavy rails.Detection results of scratching defects show that the improved Roberts operator can attain accurate positioning to defect contour and get complete edge information.Meanwhile,a decreasing amount of interference noises as well as more precise characteristic parameters of the extracted defects can also be confirmed for the improved algorithm.Furthermore,the BP neural network adopted for defects classification with the improved Roberts operator can obtain the target training precision with 98 iterative steps and time of 2s while that of traditional Roberts operator is 118 steps and 4s.Finally,an enhanced defects identification rate of 13.33%has also been confirmed after the Roberts operator is improved.The proposed detecting platform will be positive in producing high-quality heavy rails and guaranteeing the national transportation safety.展开更多
To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other relat...To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ANN） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.展开更多
Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an...Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an improved nonlinear particle swarm optimization was employed for training FNN.The experiment results on logistics formulation demonstrates the feasibility and the efficiency of this FNN model.展开更多
Recently, the frequent extreme natural disasters made enormous damage to the electric grid leading to blackouts. The lifeline system aiming at providing continuous power supply for the important load in extreme natura...Recently, the frequent extreme natural disasters made enormous damage to the electric grid leading to blackouts. The lifeline system aiming at providing continuous power supply for the important load in extreme natural disasters was designed in that condition. In this paper, a developed model for planning of the transformer substation in lifeline system which considered the effect of existing transformer substations, the motivated areas and punishment areas was proposed. The Hopfield NN （neural network） was adopted to solve the feeders and the PSO （particle swarm optimization） was adopted to new the locations of the transformer substations based on the feeders. The planning result not only took fully use of the existing substation but also got the suitable location for new construction which was satisfactory.展开更多
A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low ...A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.展开更多
Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical propertie...Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.展开更多
基金Supported by a Grant from the Science and Technology Project ofYunnan Province(2006NG02)~~
文摘By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.
基金Project(50175034) supported by the National Natural Science Foundation of China
文摘In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.
文摘A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover operator, mutation operators and adaptive probabilities for these operators. The algorithm is tested by two generally used functions and is used in training a neural network for image recognition. Experimental results show that the algorithm is an efficient global optimization algorithm.
基金Supported by the National Natural Science Foundation of China(No.51174151)Major Scientific Research Projects of Hubei Provincial Department of Education(No.2010Z19003)+1 种基金Natural Science Foundation of Science and Technology Department of Hubei Province(No.2010CDB03403)Student Research Fund of WUST(No.14ZRB047)
文摘An experimental platform accompanying with the improved Roberts algorithm has been developed to achieve accurate and real-time edge detection of surface defects on heavy rails.Detection results of scratching defects show that the improved Roberts operator can attain accurate positioning to defect contour and get complete edge information.Meanwhile,a decreasing amount of interference noises as well as more precise characteristic parameters of the extracted defects can also be confirmed for the improved algorithm.Furthermore,the BP neural network adopted for defects classification with the improved Roberts operator can obtain the target training precision with 98 iterative steps and time of 2s while that of traditional Roberts operator is 118 steps and 4s.Finally,an enhanced defects identification rate of 13.33%has also been confirmed after the Roberts operator is improved.The proposed detecting platform will be positive in producing high-quality heavy rails and guaranteeing the national transportation safety.
文摘To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ANN） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.
基金National Natural Science Foundation of China(No.60873179)Doctoral Program Foundation of Institutions of Higher Education of China(No.20090121110032)+3 种基金Shenzhen Science and Technology Research Foundations,China(No.JC200903180630A,No.ZYB200907110169A)Key Project of Institutes Serving for the Economic Zone on the Western Coast of the Tai wan Strait,ChinaNatural Science Foundation of Xiamen,China(No.3502Z2093018)Projects of Education Depart ment of Fujian Province of China(No.JK2009017,No.JK2010031,No.JA10196)
文摘Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an improved nonlinear particle swarm optimization was employed for training FNN.The experiment results on logistics formulation demonstrates the feasibility and the efficiency of this FNN model.
文摘Recently, the frequent extreme natural disasters made enormous damage to the electric grid leading to blackouts. The lifeline system aiming at providing continuous power supply for the important load in extreme natural disasters was designed in that condition. In this paper, a developed model for planning of the transformer substation in lifeline system which considered the effect of existing transformer substations, the motivated areas and punishment areas was proposed. The Hopfield NN （neural network） was adopted to solve the feeders and the PSO （particle swarm optimization） was adopted to new the locations of the transformer substations based on the feeders. The planning result not only took fully use of the existing substation but also got the suitable location for new construction which was satisfactory.
基金This work is supported by National Natural Science Foundation of China （50776005）.
文摘A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.
基金supported by the National Natural Science Foundation of China(Grant Nos.51379161&51509190)China Postdoctoral Science Foundation(Grant No.2015M572195)the Fundamental Research Funds for the Central Universities
文摘Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.
