Error-Driven Chained Multiple-Subnetwork Echo State Network for Time-Series Prediction

Huang, Jian; Li, Yiran; Shardt, Yuri A.W.; Qiao, Liang; Shi, Mingrui; Yang, Xu

doi:10.1109/jsen.2022.3200069

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…As shown in figure 5, the Santa Fe laser time series [28] is a sequence of pulsed transverse lasers with disordered periods characterized by multiple time scales and the presence of numerical rounding noise. This makes it very difficult to make accurate one-step predictions, especially near the inflection point [29]. This experimental task is a one-step prediction, i.e.…”

Section: Santa Fe Laser Time Seriesmentioning

confidence: 99%

Semi-supervised Echo State Network with Partial Correlation Pruning for Time-Series Variables Prediction in Industrial Processes

Huang¹,

Wang²,

Yang³

et al. 2023

Meas. Sci. Technol.

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For an ordinary echo state network (ESN), redundant information in the huge reservoir will lead to degradation of the prediction performance of the network, especially when the labels of the samples are limited. To solve this problem, a semi-supervised ESN with partial correlation pruning (PCP-S2ESN) is proposed in this paper to scientifically capture the essential association between two reservoir variables while controlling for the influence of other factors. In this way, redundant neurons and their connection weights in the reservoir are eliminated, so that the prediction accuracy is significantly enhanced by optimizing the network structure. Moreover, anunsupervised pre-training procedure is introduced to modify the input weight matrix and reservoir connection weight matrix of the ESN, which successfully achieves precise prediction of time-series variables with limited labels. The superiority of the PCP-S2ESN model is demonstrated through two benchmark prediction tasks and the fed-batch penicillin cultivation (FBPC) process.

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Section: Santa Fe Laser Time Seriesmentioning

confidence: 99%

Semi-supervised Echo State Network with Partial Correlation Pruning for Time-Series Variables Prediction in Industrial Processes

Huang¹,

Wang²,

Yang³

et al. 2023

Meas. Sci. Technol.

Self Cite

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“…The reservoir is the core factor that determines the performance of an ESN. There have been many attempts to find more efficient reservoir schemes to improve the performance of ESNs, for example, the reservoir structure [13][14][15][16][17], the type of reservoir neurons [18,19], reservoir parameter optimization [20][21][22], obtaining echo state property (ESP) condition [23,24], etc.…”

Section: Introductionmentioning

confidence: 99%

A Multireservoir Echo State Network Combined with Olfactory Feelings Structure

Lun,

Wang,

Cai

et al. 2023

Electronics

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As a special form of recurrent neural network (RNN), echo state networks (ESNs) have achieved good results in nonlinear system modeling, fuzzy nonlinear control, time series prediction, and so on. However, the traditional single-reservoir ESN topology limits the prediction ability of the network. In this paper, we design a multireservoir olfactory feelings echo state network (OFESN) inspired by the structure of the Drosophila olfactory bulb, which provides a new connection mode. The connection between subreservoirs is transformed into the connection between each autonomous neuron, the neurons in each subreservoir are sparsely connected, and the neurons in different subreservoirs cannot communicate with each other. The OFESN greatly simplifies the coupling connections between neurons in different libraries, reduces information redundancy, and improves the running speed of the network. The findings from the simulation demonstrate that the OFESN model, as introduced in this study, enhances the capacity to approximate sine superposition function and the Mackey–Glass system when combined. Additionally, this model exhibits improved prediction accuracy by 98% in some cases and reduced fluctuations in prediction errors.

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Chemical process fault detection and trend analysis based on KESN

Cao,

Cheng,

Deng

et al. 2024

Can J Chem Eng

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Fault detection has great significance for chemical process safety with the development of science and technology. The conventional echo state network‐based fault detection method does not highlight key fault features, and cannot forecast future fault trend after the occurrence of faults. For the above problems, a chemical process fault detection and trend analysis strategy based on key feature enhanced echo state network (KESN) is proposed. First, dynamic features are extracted by a detecting echo state network. Then, a weighting strategy is designed to enhance key features and increase fault detection rates. After detecting a fault, independent component analysis is utilized to extract independent key features. Future fault trend is forecasted based on the forecasting multi‐KESN. Simulation results on the Tennessee Eastman process demonstrate the effectiveness of the proposed method.

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Error-Driven Chained Multiple-Subnetwork Echo State Network for Time-Series Prediction

Cited by 6 publications

References 28 publications

Semi-supervised Echo State Network with Partial Correlation Pruning for Time-Series Variables Prediction in Industrial Processes

Semi-supervised Echo State Network with Partial Correlation Pruning for Time-Series Variables Prediction in Industrial Processes

A Multireservoir Echo State Network Combined with Olfactory Feelings Structure

Chemical process fault detection and trend analysis based on KESN

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