Journal ArticleDOI
Hybrid neural network models of transducers
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TLDR
The results show that the hybrid NN approach can significantly improve modeling precision in comparison with the conventional modeling method and is superior to NN black-box modeling because the former possesses smaller network scale, higher convergence speed, higher model precision and better generalization performance.Abstract:
A hybrid neural network (NN) approach is proposed and applied to modeling of transducers in the paper. The modeling procedures are also presented in detail. First, the simulated studies on the modeling of single input?single output and multi input?multi output transducers are conducted respectively by use of the developed hybrid NN scheme. Secondly, the hybrid NN modeling approach is utilized to characterize a six-axis force sensor prototype based on the measured data. The results show that the hybrid NN approach can significantly improve modeling precision in comparison with the conventional modeling method. In addition, the method is superior to NN black-box modeling because the former possesses smaller network scale, higher convergence speed, higher model precision and better generalization performance.read more
Citations
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Journal ArticleDOI
Hybrid calibration method for six-component force/torque transducers of wind tunnel balance based on support vector machines
TL;DR: From the calibrating results, it can be indicated that the SVM hybrid calibration method has improved the calibration accuracy significantly without increasing data samples, compared with calibration matrix.
Journal ArticleDOI
Measurement and calibration method for an optical encoder based on adaptive differential evolution-Fourier neural networks
TL;DR: The method based on the adaptive differential evolution-Fourier neural network (ADE-FNN) is proposed to improve the accuracy of optical encoders and introduces an ADE algorithm to optimize the weights of the FNN.
Journal ArticleDOI
Characterization of the displacement response in chromatic confocal microscopy with a hybrid radial basis function network.
Wenlong Lu,Cheng Chen,Jian Wang,Richard Leach,Chi Zhang,Xiaojun Liu,Lei Zili,Wenjun Yang,Xiangqian Jane Jiang +8 more
TL;DR: Using experimental tests, it is shown that the hybrid radial basis function network method significantly improves the measurement accuracy, when compared to the existing characterizing methods.
Journal ArticleDOI
Hybrid modeling approach for vehicle frame coupled with nonlinear dampers
TL;DR: A hybrid model for vehicle frame system is established and successfully validated via a dummy vehicle riding in different conditions and the results show that the hybrid model can capture the nonlinear dynamic characteristics accurately.
Proceedings ArticleDOI
ANFIS parallel hybrid modeling method for optical encoder calibration
TL;DR: A novel hybrid modeling method, combining the knowledge-based model and adaptive-network-based fuzzy inference system (ANFIS) model, is proposed in this paper and significant improvements regarding the measurement accuracy of the encoder are achieved.
References
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Book
Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
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Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.