This paper presents a structured methodology for local network design engineering (SMLNDE). A complex and fuzzy project for local network design can be decomposed into a set of simple and particular activities using t...This paper presents a structured methodology for local network design engineering (SMLNDE). A complex and fuzzy project for local network design can be decomposed into a set of simple and particular activities using the SMLNDE. The SMLNDE allows rigorous requirements definition and permits the exhaustive consideration of the large number of factors influencing local network design engineering. The complete and clear design documentations and an optimal design can also be provided by the methodology. The SMLNDE has been implemented using the structured analysis and design technique. The study shows that the SMLNDE is an effective design methodology for the large and complex local networks.展开更多
Neural networks are being used to construct meta-models in numerical simulation of structures.In addition to network structures and training algorithms,training samples also greatly affect the accuracy of neural netwo...Neural networks are being used to construct meta-models in numerical simulation of structures.In addition to network structures and training algorithms,training samples also greatly affect the accuracy of neural network models.In this paper,some existing main sampling techniques are evaluated,including techniques based on experimental design theory, random selection,and rotating sampling.First,advantages and disadvantages of each technique are reviewed.Then,seven techniques are used to generate samples for training radial neural networks models for two benchmarks:an antenna model and an aircraft model.Results show that the uniform design,in which the number of samples and mean square error network models are considered,is the best sampling technique for neural network based meta-model building.展开更多
文摘This paper presents a structured methodology for local network design engineering (SMLNDE). A complex and fuzzy project for local network design can be decomposed into a set of simple and particular activities using the SMLNDE. The SMLNDE allows rigorous requirements definition and permits the exhaustive consideration of the large number of factors influencing local network design engineering. The complete and clear design documentations and an optimal design can also be provided by the methodology. The SMLNDE has been implemented using the structured analysis and design technique. The study shows that the SMLNDE is an effective design methodology for the large and complex local networks.
基金Specialized Research Fund for the Doctoral Program of Higher Education,China (No.20010227012)
文摘Neural networks are being used to construct meta-models in numerical simulation of structures.In addition to network structures and training algorithms,training samples also greatly affect the accuracy of neural network models.In this paper,some existing main sampling techniques are evaluated,including techniques based on experimental design theory, random selection,and rotating sampling.First,advantages and disadvantages of each technique are reviewed.Then,seven techniques are used to generate samples for training radial neural networks models for two benchmarks:an antenna model and an aircraft model.Results show that the uniform design,in which the number of samples and mean square error network models are considered,is the best sampling technique for neural network based meta-model building.
