Performance of soft sensors based on stochastic configuration networks with nonnegative garrote

A robust transfer deep stochastic configuration network for industrial data modeling is proposed to address challenging problems such as the presence of outliers (or noise) and conditional drift of the data model due to changes in working conditions. Assuming that outliers follow the t-distribution, the maximum a posteriori estimation is employed to evaluate the read-out weights, and the expectation maximization algorithm is used to iteratively optimize the hyperparameters of the distribution. Moreover, the knowledge contained in the data are expressed in the form of the model structure, connection weights and outlier distribution, and a knowledge-data-based robust transfer strategy is developed to offset the impact of insufficient training data on the learning performance of a deep stochastic configuration network with a new working condition. Comparative experiments are carried out using the historical furnace temperature data of a municipal solid waste incineration plant in China. The results show that the proposed method performs more favorably in robust data modeling and mitigates the impact of changes in working conditions on the applicability and accuracy.

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A robust transfer deep stochastic configuration network for industrial data modeling

Guo

Yan

Tang

2023

Industrial Artificial Intelligence

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Universal approximation property of stochastic configuration networks for time series

Zhang,

Zhao,

Zhang

2024

Industrial Artificial Intelligence

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For the purpose of processing sequential data, such as time series, and addressing the challenge of manually tuning the architecture of traditional recurrent neural networks (RNNs), this paper introduces a novel approach-the Recurrent Stochastic Configuration Network (RSCN). This network is constructed based on the random incremental algorithm of stochastic configuration networks. Leveraging the foundational structure of recurrent neural networks, our learning model commences with a modest-scale recurrent neural network featuring a single hidden layer and a solitary hidden node. Subsequently, the node parameters of the hidden layer undergo incremental augmentation through a random configuration process, with corresponding weights assigned structurally. This iterative expansion continues until the network satisfies predefined termination criteria. Noteworthy is the adaptability of this algorithm to handle time series data, exhibiting superior performance compared to traditional recurrent neural networks with similar architectures. The experimental results presented in this paper underscore the efficacy of the proposed RSCN for sequence data processing, showcasing its advantages over conventional recurrent neural networks in the context of the performed experiments.

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A sparse regularized soft sensor based on GRU and self-interpretation double nonnegative garrote: From variable selection to structure optimization

Sui,

Sun,

Liu

et al. 2024

Control Engineering Practice

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Performance of soft sensors based on stochastic configuration networks with nonnegative garrote

Cited by 9 publications

References 37 publications

A robust transfer deep stochastic configuration network for industrial data modeling

A robust transfer deep stochastic configuration network for industrial data modeling

Universal approximation property of stochastic configuration networks for time series

A sparse regularized soft sensor based on GRU and self-interpretation double nonnegative garrote: From variable selection to structure optimization

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