A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at u...A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.展开更多
The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated b...The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated by some adequate methods. In this paper, a nonlinear model of dynamic thrust measurement system is established by using radial basis function neural network （RBF-NN）, where a novel multi-step force generator is designed to stimulate the nonlinearity of the system, and a practical compensation method for the measurement system using left inverse model is proposed. Left inverse model can be considered as a perfect dynamic compensation of the dynamic thrust measurement system, and in practice, it can be approximated by RBF-NN based on least mean square （LMS） algorithms. Different weights are set for producing the multi-step force, which is the ideal input signal of the nonlinear dynamic thrust measurement system. The validity of the compensation method depends on the engine＇s performance and the tolerance error 0.5%, which is commonly demanded in engineering. Results from simulations and experiments show that the practical compensation using left inverse model based on RBF-NN in dynamic thrust measuring system can yield high tracking accuracy than the conventional methods.展开更多
基金The National Natural Science Foundation of China(No.61261007,61062005)the Key Program of Yunnan Natural Science Foundation(No.2013FA008)
文摘A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.
文摘The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated by some adequate methods. In this paper, a nonlinear model of dynamic thrust measurement system is established by using radial basis function neural network （RBF-NN）, where a novel multi-step force generator is designed to stimulate the nonlinearity of the system, and a practical compensation method for the measurement system using left inverse model is proposed. Left inverse model can be considered as a perfect dynamic compensation of the dynamic thrust measurement system, and in practice, it can be approximated by RBF-NN based on least mean square （LMS） algorithms. Different weights are set for producing the multi-step force, which is the ideal input signal of the nonlinear dynamic thrust measurement system. The validity of the compensation method depends on the engine＇s performance and the tolerance error 0.5%, which is commonly demanded in engineering. Results from simulations and experiments show that the practical compensation using left inverse model based on RBF-NN in dynamic thrust measuring system can yield high tracking accuracy than the conventional methods.
