A Novel Method of Curve Fitting Based on Optimized Extreme Learning Machine

Li, Michael M.; Li, Lily D.

doi:10.1080/08839514.2020.1787677

Cited by 14 publications

(9 citation statements)

References 19 publications

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“…Besides, if we fitted curve fitting equation with pneumoconiosis cases from 2000 to 2018, it was: y∘=−13.065x2+53,268x−5,498,980, average fitting relative error was 26.45%, relative error of prediction was 72.62%, coefficient of determination was 0.700, and the predicted value seriously deviated from the actual value. Therefore, the distribution type of data from 2000 to 2018 did not follow the application of Curve Fitting Method 42 . We tried to establish various possible curve fitting equations using data from different periods as the previous curve fitting equation.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the distribution type of data from 2000 to 2018 did not follow the application of Curve Fitting Method. 42 We tried to establish various possible curve fitting equations using data from different periods as the previous curve fitting equation. Finally, we utilized data of diagnosed pneumoconiosis cases in China from 2008 to 2018 to fit the curve fitting equation, and predicted the incidence trend of pneumoconiosis in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Curve Fitting Method is a method of describing discrete data with an analytical expression by approximating or comparing the functional relationship between coordinates represented by a set of discrete points on a plane with a continuous curve. 42 Curve Fitting Method was very suitable for nonlinear trend of data, when there is a nonlinear tend between the dependent variable and independent variables in the scatter plot, the quantitative of dependence relationship between the two variables can be reflected by Curve Fitting Method. 43 In this study, we made a scatter plot for X (year) and Y (diagnosed pneumoconiosis cases), which showed that some data could be fitted by Curve Fitting Method.…”

Section: Curve Fitting Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019

Zhou

Zhu²,

Li³

et al. 2021

Journal of Occupational &Amp; Environmental Medicine

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Objective:To predict the future incidence trend of pneumoconiosis in China, and to evaluate three predictive models.Methods:We selected pneumoconiosis cases (2000–2019) to fit Generalized Additive Model (GAM), Curve Fitting Method, and GM (1,1) Model, chosen average fitting relative error, relative error of prediction, and coefficient of determination to evaluate models.Results:Chinese incidence trend of pneumoconiosis would decrease in the future. Predicted value of GAM (14,566) and Curve Fitting Method (15,781) in 2019 was close to the actual value (15,898). Relative error of prediction of GAM and Curve Fitting Method was −8.38% and −0.73%, respectively.Conclusions:The government needs to strengthen prevention and control since pneumoconiosis cases might remain huge in the future. Besides, we advise that GAM and Curve Fitting Method can be used to predict Chinese incidence trend of pneumoconiosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Curve Fitting Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019

Zhou

Zhu²,

Li³

et al. 2021

Journal of Occupational &Amp; Environmental Medicine

View full text Add to dashboard Cite

show abstract

“…14 It is the process of transforming the observed relationship in a particular dataset into a parameterized function and can be used for prediction and parameter estimation. 15 Curve fitting method constructs a curve or mathematical function of the number of patients to predict the future disease trends by best fitting a series of data points of the number of patients in the past. 16 Curve fitting methods include several classes, such as polynomials, exponential functions, power functions and logarithmic functions.…”

Section: Curve Fitting Methodsmentioning

confidence: 99%

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China

Teng,

Jian,

Chen

et al. 2023

COPD

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To predict the future number of patients with chronic obstructive pulmonary disease (COPD) in China and compare the three prediction models. Methods: A generalized additive model (GAM), autoregressive integrated moving average (ARIMA) model, and curve-fitting method were used to fit and predict the number of patients with COPD in China. Data on the number of patients with COPD in China from 1990 to 2019 were obtained from the Global Burden of Disease (GBD) database. The coefficient of determination (R 2 ), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), relative error of prediction, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and compare the fitting effect, prediction effect, and reliability of the three models. Results: The GAM, ARIMA, and curve-fitting methods could predict future trends in COPD in China. The performance of the GAM is the best among the three models, whereas the curve fitting method is the worst, and the ARIMA (0,1,2) model is in between. The prediction results of the three models showed that the number of patients with COPD in China is expected to increase from 2020 to 2025. Conclusion: GAM and AIRMA models are recommended for predicting the future prevalence of COPD in China. The number of patients with COPD in China is expected to increase in the next few years. The prevention and control of COPD in China still needs to be strengthened. Using appropriate models to predict future trends in COPD will provide support for health policymakers.

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“…Generally, LM based backpropagation technique was employed for the curve-fitting problem. 67 Here, the number of epochs was set as 1000. This methodology is prepared for estimating the solar irradiance as well as wind speed respectively under different environmental conditions.…”

Section: Data Collection and Training Approachmentioning

confidence: 99%

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

2022

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The rapid growth of hybrid renewable Distributed Energy Resources (DERs) generation possess various challenges with inaccurate forecast models in stochastic power systems. The prime objective of this research is to maximum utilization of scheduled power from hybrid renewable based DERs to maintain the load-demand profile with reduce distributed grid burden. The proposed mixed input-based cascaded artificial neural network [Formula: see text] is realized for the prediction of a short-term based hourly solar irradiance and wind speed. The testing approach is performed through a historical hourly dataset of the proposed site. Further, the normalized data sets are divided into hourly-based samples for validating the load demand power with respect to the variation in metrological data. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is simulated for short-term power demand prediction. This adaptive methodology is an effective approach for load-demand management which is based on cross-entropy. It also confirmed that during testing, the forecasting mean error and cross-entropy are less than 5% under a specific time slap of an individual day. The regression analysis is performed through the time series fitting simulation tool at different time horizons. The performance evaluation of the designed model is compared with the multi-layer perceptron model. Simulation results display the proposed mixed input-based cascaded system has enhanced accuracy and optimal performance than the multi-output correlated perceptron model.

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A Novel Method of Curve Fitting Based on Optimized Extreme Learning Machine

Cited by 14 publications

References 19 publications

Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019

Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

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