In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochast...In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochastic approximation algorithm is proposed by integrating neural network with fuzzy simulation. At first, fuzzy simulation is used to generate a set of input-output data. Then a neural network is trained according to the set. Finally, the trained neural network is embedded in simultaneous perturbation stochastic approximation algorithm. Simultaneous perturbation stochastic approximation algorithm is used to search the optimal solution. Two numerical examples are presented to illustrate the effectiveness of the proposed algorithm.展开更多
In past years,growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array.An appealing requirement on such sparse array arranged withi...In past years,growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array.An appealing requirement on such sparse array arranged within a specified survey region is that to make the number of sensor elements as small as possible,meanwhile without deteriorating imaging quality.For this end,we propose a novel methodology of arranging sensors in an optimal manner,exploring the concept of information capacity developed originally in the communication society.The proposed scheme reduces mathematically the design of a sparse sensor array into solving a combinatorial optimization problem,which can be resolved efficiently using widely adopted Simultaneous Perturbation and Statistical Algorithm(SPSA).Three sets of numerical examples of designing optimal sensor array are provided to demonstrate the performance of proposed methodology.展开更多
基金National Natural Science Foundation of China (No.70471049)China Postdoctoral Science Foundation (No. 20060400704)
文摘In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochastic approximation algorithm is proposed by integrating neural network with fuzzy simulation. At first, fuzzy simulation is used to generate a set of input-output data. Then a neural network is trained according to the set. Finally, the trained neural network is embedded in simultaneous perturbation stochastic approximation algorithm. Simultaneous perturbation stochastic approximation algorithm is used to search the optimal solution. Two numerical examples are presented to illustrate the effectiveness of the proposed algorithm.
文摘In past years,growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array.An appealing requirement on such sparse array arranged within a specified survey region is that to make the number of sensor elements as small as possible,meanwhile without deteriorating imaging quality.For this end,we propose a novel methodology of arranging sensors in an optimal manner,exploring the concept of information capacity developed originally in the communication society.The proposed scheme reduces mathematically the design of a sparse sensor array into solving a combinatorial optimization problem,which can be resolved efficiently using widely adopted Simultaneous Perturbation and Statistical Algorithm(SPSA).Three sets of numerical examples of designing optimal sensor array are provided to demonstrate the performance of proposed methodology.
