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Jian Pan,Xiao Jiang,Xiangkui Wan,Wenfang Ding 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.3
For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressivemoving average system), this paper proposes a filtering based extended stochastic gradient (ESG) algorithm anda filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy. The key isusing the filtering technique and the multi-innovation identification theory. The proposed algorithms can identifythe parameters of the system model and the noise model. The filtering based multi-innovation ESG algorithm cangive more accurate parameter estimates. The numerical simulation results demonstrate that the proposed algorithmswork well.
Feng Ding,Lei Lv,Jian Pan,Xiangkui Wan,Xue-Bo Jin 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.4
This paper considers the parameter identification problems of controlled autoregressive systems using observation information. According to the hierarchical identification principle, we decompose the controlled autoregressive system into two subsystems by introducing two fictitious output variables. Then a two-stage gradientbased iterative algorithm is proposed by means of the iterative technique. In order to improve the performance of the tracking the time-varying parameters, we derive a two-stage multi-innovation gradient-based iterative algorithm based on the multi-innovation identification theory. Finally, an example is provided to illustrate the effectiveness of the proposed algorithms.
Jian Pan,Sunde Liu,Jun Shu,Xiangkui Wan 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.12
This paper considers the parameter identification problems of a Volterra nonlinear system. In order to overcome the excessive calculation amount of the Volterra systems, a hierarchical least squares algorithm is proposed through combining the hierarchical identification principle. The key is to decompose the Volterra systems into three subsystems with a smaller number of parameters and to estimates the parameters of each subsystem, respectively. The calculation analysis indicates that the proposed algorithm has less computational cost than the recursive least squares algorithm. Finally, the simulation results indicate that the proposed algorithm are effective for identifying Volterra systems.
( Zheheng Rao ),( Chunyan Zeng ),( Minghu Wu ),( Zhifeng Wang ),( Nan Zhao ),( Min Liu ),( Xiangkui Wan ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.1
Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.