Gated Broad Learning System Based on Deep Cascaded for Soft Sensor Modeling of Industrial Process

Mou, Miao; Zhao, Xiaoqiang

doi:10.1109/tim.2022.3170967

Cited by 30 publications

(6 citation statements)

References 42 publications

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“…Due to its strong generalization capability and superior learning efficiency, BLS has attracted extensive attentions from both academia and industry, and various improvements and expansions have been made to it [4]. As a novel neural network model, BLS has much room for network structure expansion, and many BLS-based structural variants have been constantly proposed, such as cascade BLS [5], deep cascade BLS [6], intergroup cascade BLS [7], stacked BLS [8], recurrent BLS and gated BLS [9,10], cascade BLS with dropout or dense connection [11], incremental multilayer BLS [12], etc. By introducing Takagi-Sugeno fuzzy systems into BLS, a new fuzzy BLS was established [13] and studied deeply [14][15][16][17][18].…”

Section: Related Workmentioning

confidence: 99%

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RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure

Wei

Zhou

et al. 2023

Electronics

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Recently, the broad learning system (BLS) has been widely developed in virtue of its excellent performance and high-computational efficiency. However, two deficiencies still exist in BLS and preclude its deployment in real applications. First, the standard BLS performs poorly in outlier environments because the least squares loss function it uses to train the network is sensitive to outliers. Second, the model structure of BLS is likely to be redundant since the hidden nodes in it are randomly generated. To address the above two issues, a new robust and compact BLS (RCBLS), based on M-estimator and sparsity regularization, is proposed in this paper. The RCBLS develops from the BLS model and maintains its excellent characteristics, but replaces the conventional least squares learning criterion with an M-estimator-based loss function that is less sensitive to outliers, in order to suppress the incorrect feedback of the model to outlier samples, and hence enhance its robustness in the presence of outliers. Meanwhile, the RCBLS imposes the sparsity-promoting l2,1 -norm regularization instead of the common l2-norm regularization for model reduction. With the help of the row sparsity of l2,1-norm regularization, the unnecessary hidden nodes in RCBLS can be effectively picked out and removed from the network, thereby resulting in a more compact network. The theoretical analyses on outlier robustness, structural compactness and computational complexity of the proposed RCBLS model are provided. Finally, the validity of the RCBLS is verified by regression, time series prediction and image classification tasks. The experimental results demonstrate that the proposed RCBLS has stronger anti-outlier ability and more compact network structure than BLS and other representative algorithms.

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Section: Related Workmentioning

confidence: 99%

“…, N are the Lagrangian multipliers for the i-th equality constraint in Equation ( 9). Taking the derivatives of L(W, E, A) in Equation (10), with respect to W, e i , a i , respectively, and setting the derivatives to zero, we arrive at:…”

Section: Optimization Of Rcblsmentioning

confidence: 99%

RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure

Wei

Zhou

et al. 2023

Electronics

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“…In the context of Industry 4.0, the manufacturing industry has achieved rapid development, and product quality is the most important production indicator in the manufacturing process [1][2][3][4]. According to whether the faults affect the quality of the products, the faults are divided into quality-related faults and quality-unrelated faults.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Quality‐Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process

Liu,

Zhao,

Hui

et al. 2024

Chem Eng & Technol

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Quality‐related fault detection has become a hot research topic in recent years. It is not reliable to measure quality‐related relationships only by mutual information among process variables and single‐quality variables. Frequent alarms for quality‐unrelated faults seriously affect the normal operation of industrial production. At the same time, the strong nonlinearity of the process data leads to the difficulty of feature extraction. In this paper, we propose a fault detection algorithm based on nonlinear quality‐related neighborhood embedding neural orthogonal mapping (QR‐NENOM). First, quality‐related and quality‐unrelated variables are selected by Bayesian fusion mutual information, and the weighted method of mutual information is used to enhance the quality‐related information and suppress the quality‐unrelated information. Second, local manifold information is obtained by reconstructing nearest neighbors of process data, and key features are extracted by the nonlinear method composed of neural network and orthogonal mapping. Then, statistical indicators are established to complete fault detection. Finally, the nonlinear feature extraction ability of NENOM is verified by numerical examples, and the QR‐NENOM algorithm proposed in this paper is applied to the penicillin fermentation process. Comparative experiments show that QR‐NENOM has better detection performance for quality‐related faults and fewer alarms for quality‐unrelated faults.

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“…A batch process fault detection method for multi-stage broad learning system was proposed by Chang Peng 20 . Miao Mou proposed a Gated broad learning system based on deep cascaded for soft sensor modeling of industrial process 21 . Wenkai Hu introduces a novel fault diagnosis method utilizing a weighted timeliness Broad Learning System (BLS) with multi-feature extraction, enhancing process safety in modern industrial facilities by effectively addressing complex signal variations and interrelated faults 22 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive Soft Sensor of Stacking Approximate Kernel- Based Broad Learning System for Batch Process

Zhao,

Yang,

et al. 2024

Preprint

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To deal with the highly nonlinear and time-varying characteristics of batch process, a model named Moving Window Stacking Approximate Kernel-Based Broad Learning System (MW-Stacking-AKBLS) is proposed in this paper. This model innovatively introduces the AKBLS algorithm and the MW-Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The MW-Stacking framework adopts the Stacking ensemble learning method, integrating multiple ABKLS models to enhance the model's generalization ability. Additionally, by adopting the moving window method, the model has been equipped with adaptive ability to better adapt to slow changes in industrial batch process. Finally, comparative experimental results on a substantial dataset of penicillin simulations indicate a significant improvement in predictive accuracy for the proposed MW-Stacking AKBLS model compared to other commonly used algorithms.

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Gated Broad Learning System Based on Deep Cascaded for Soft Sensor Modeling of Industrial Process

Cited by 30 publications

References 42 publications

RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure

RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure

Nonlinear Quality‐Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process

Adaptive Soft Sensor of Stacking Approximate Kernel- Based Broad Learning System for Batch Process

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