Evolving LSTM Networks for Time-Series Classification in EdgeIoT
We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the...
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Veröffentlicht in: | Mathematical problems in engineering 2023-01, Vol.2023 (1) |
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creator | Cui, Pei Li, San Jiang, Kaina Liu, Zhendong Sun, Xingkai |
description | We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC-optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing. |
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subjects | Accuracy Algorithms Civilization Classification Data mining Deep learning Genetic algorithms Intelligence Neural networks Optimization Optimization algorithms Simulation Society Target recognition Time series Unmanned aerial vehicles |
title | Evolving LSTM Networks for Time-Series Classification in EdgeIoT |
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