PCA-CIPSO-GRNN-based Cyclic Cooling Water Corrosion Prediction

Dong, Chaoyang; Feng, Zeyao; Li, Jingxian

doi:10.1109/icma.2019.8816566

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…In [135], the researcher presented the GA and LS-based SVM method to control fractional-order systems, which achieved better effectiveness and feasibility than the conventional LS-based SVM. The authors of [139] proposed the principal component analysis using the chaotic immune PSO tuned GRNN for forecasting the corrosion of circulating cooling water in a petrochemical enterprise. The approach achieved better forecasting accuracy and convergence speed than the traditional PSO-tuned GRNN model.…”

Section: Optimization Algorithms With Ann-based Forecasting Approachesmentioning

confidence: 99%

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Ramadevi

Bingi

2022

Symmetry

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Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.

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Section: Optimization Algorithms With Ann-based Forecasting Approachesmentioning

confidence: 99%