Minimax probability machine regression （MPMR） was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε ...Minimax probability machine regression （MPMR） was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε to the true regression function. After exploring the principle of MPMR, and verifying the chaotic property of the load series from a certain power system, one-day-ahead predictions for 24 time points next day wcre done with MPMR. Thc results demonstrate that MPMP has satisfactory prediction efficiency. Kernel function shape parameter and regression tube value may influence the MPMR-based system performance. In the experiments, cross validation was used to choose the two parameters.展开更多
In order to improve the precision of the target detection in wireless sensor networks,a new approach based on genetic algorithm(GA)was proposed to optimize the placement of the sensor.The target location problem was t...In order to improve the precision of the target detection in wireless sensor networks,a new approach based on genetic algorithm(GA)was proposed to optimize the placement of the sensor.The target location problem was transformed into locating a target at a grid point through modeling the sensor field as a grid of points.Moreover,the sensor placement problem was formulated as a combinatorial optimization problem,which is aimed at minimizing the maximum discrimination error under the restraints of limited cost and complete coverage.The GA approach uses binary coding to represent the location,and both single parent crossover operator and single parent mutation operator are used to improve its speed and efficiency.Experimental results have shown that a global optimal solution can be quickly obtained using the proposed method and the precision requirement for target location is satisfied.展开更多
基金The research was supported by the Science & Research Foundation of East China Jiaotong University (No.23)
文摘Minimax probability machine regression （MPMR） was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε to the true regression function. After exploring the principle of MPMR, and verifying the chaotic property of the load series from a certain power system, one-day-ahead predictions for 24 time points next day wcre done with MPMR. Thc results demonstrate that MPMP has satisfactory prediction efficiency. Kernel function shape parameter and regression tube value may influence the MPMR-based system performance. In the experiments, cross validation was used to choose the two parameters.
基金supported by the Hi-Tech Research and Development Program of China(No.2003AA148010).
文摘In order to improve the precision of the target detection in wireless sensor networks,a new approach based on genetic algorithm(GA)was proposed to optimize the placement of the sensor.The target location problem was transformed into locating a target at a grid point through modeling the sensor field as a grid of points.Moreover,the sensor placement problem was formulated as a combinatorial optimization problem,which is aimed at minimizing the maximum discrimination error under the restraints of limited cost and complete coverage.The GA approach uses binary coding to represent the location,and both single parent crossover operator and single parent mutation operator are used to improve its speed and efficiency.Experimental results have shown that a global optimal solution can be quickly obtained using the proposed method and the precision requirement for target location is satisfied.
