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Spatiotemporal Prediction for Energy System of Steel Industry by Generalized Tensor Granularity Based Evolving Type-2 Fuzzy Neural Network | IEEE Journals & Magazine | IEEE Xplore

Spatiotemporal Prediction for Energy System of Steel Industry by Generalized Tensor Granularity Based Evolving Type-2 Fuzzy Neural Network


Abstract:

Multiscale prediction analysis for the generation and consumption of by-product gas flows in various devices from the various production regions of the steel industry can...Show More

Abstract:

Multiscale prediction analysis for the generation and consumption of by-product gas flows in various devices from the various production regions of the steel industry can be regarded as the prerequisite for energy scheduling and allocation. In this article, a generalized tensor granularity (GTG) based evolving interval type-2 (IT2) fuzzy neural network (GTG-EIT2FNN) is proposed to perform the multiscale prediction for spatio-temporal industrial data streams. A generalized IT2 fuzzy C-means clustering method is presented to extract the similarity characteristics from GTG that considers the spatial location, the semantics of manufacturing processes, the uncertainty triggered by multiple sensors, time-varying and multiscale property. Moreover, the robustness and adaptability of GTG-EIT2FNN is improved by incorporating an extended Q-learning to learn the optimal policy in terms of the input structure and network ones. A number of industrial study cases show that GTG-EIT2FNN outperforms state-of-the-art comparative algorithms in achieving the best tradeoff between accuracy and simplicity.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 17, Issue: 12, December 2021)
Page(s): 7933 - 7945
Date of Publication: 25 February 2021

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