RBFNN-Based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems

Yin, Xin; Zhang, Qichun; Wang, Hong; Ding, Zhengtao

doi:10.1109/tac.2019.2914257

Cited by 63 publications

(31 citation statements)

References 16 publications

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“…Based on the excellent performance of the presented algorithm in terms of hole defect signal recognition, future studies should be conducted to identify its ability to detect other defect types, such as cracks and foreign matter inclusions, which are also common defect types in concrete detection. Moreover, the algorithm will also be further improved from the view of non-Gaussian distribution [28] and entropybased estimation [29], [30] in order to release the Gaussian assumption in Fig. 12.…”

Section: Discussionmentioning

confidence: 99%

Defect Recognition in Concrete Ultrasonic Detection Based on Wavelet Packet Transform and Stochastic Configuration Networks

Zhao

Zheng

et al. 2021

IEEE Access

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Section: Discussionmentioning

confidence: 99%

Defect Recognition in Concrete Ultrasonic Detection Based on Wavelet Packet Transform and Stochastic Configuration Networks

Zhao

Zheng

et al. 2021

IEEE Access

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“…In feature selection process, C4.5 algorithm uses information gain rate as the standard indicator to circumvent the problem arising from ID3 algorithm [20]. The information gain rate of attribute A is expressed as:…”

Section: Plos Onementioning

confidence: 99%

Fault diagnosis method of submersible screw pump based on random forest

et al. 2020

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The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.

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“…Compared to EKF, unscented Kalman filter (UKF) [8], [9] gives a solution to enhance the accuracy of filtering. Currently, particle filter (PF) [10], entropy filter [11] and non-fragile H ∞ robust filter get extensive attentions from the view of filtering theory [12]. However, these filters take huge amount of calculation while robust filter design need to solve a Riccati equation which restricts its application.…”

Section: Introductionmentioning

confidence: 99%

An EKF-Based Performance Enhancement Scheme for Stochastic Nonlinear Systems by Dynamic Set-Point Adjustment

Tang

Zhang

2020

IEEE Access

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In this paper, a performance enhancement scheme has been investigated for a class of stochastic nonlinear systems via set-point adjustment. Considering the practical industrial processes, the multi-layer systematic structure has been adopted to achieve the control design requirements subjected to random noise. The basic loop control is given by PID design while the parameters have been fixed after the design phase. Alternatively, we can consider that there exists an unadjustable loop control. Then, the additional loop is designed for performance enhancement in terms of the tracking accuracy. In particular, a novel approach has been presented to dynamically adjust the set-points using the estimated states of the systems through extended Kalman filter (EKF). Minimising the entropy criterion, the parameters of the set-point adjustment controller can be optimised which will enhance the performance of the entire closed-loop systems. Based upon the presented scheme, the stochastic stability analysis has been given to demonstrate that the closedloop tracking errors are bounded in probability one. To indicate the effectiveness of the presented control scheme, the numerical examples have been given and the simulation results imply that the designed systems are bounded and the tracking performance can be enhanced simultaneously. In summary, a new framework for system performance enhancement has been presented even if the loop control is unadjustable which forms the main contribution of this paper.

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RBFNN-Based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems

Cited by 63 publications

References 16 publications

Defect Recognition in Concrete Ultrasonic Detection Based on Wavelet Packet Transform and Stochastic Configuration Networks

Defect Recognition in Concrete Ultrasonic Detection Based on Wavelet Packet Transform and Stochastic Configuration Networks

Fault diagnosis method of submersible screw pump based on random forest

An EKF-Based Performance Enhancement Scheme for Stochastic Nonlinear Systems by Dynamic Set-Point Adjustment

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