A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM

Cao, Shanshan; Liu, Yanbao; Wang, Yanping

doi:10.1016/s1006-1266(08)60037-1

Cited by 61 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…where x is the input, y indicates the output, ω is the weight vector with m dimension, ϕ is the mapping term, and b is the bias term [66,67]. The cost function of LSSVR can be expressed as…”

Section: Least Square Support Vector Regressionmentioning

confidence: 99%

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

et al. 2019

View full text Add to dashboard Cite

Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with substractive clustering (NF-SC)), and two heuristic regression methods (least square support vector regression (LSSVR) and M5 regression tree (M5RT)) in the prediction of hourly wind speed and wind power using a cross-validation method. Fourfold cross-validation was employed by dividing the data into four equal subsets. LSSVR’s performance was superior to that of the M5RT, NF-SC, and NF-GP models for all datasets in wind speed prediction. The overall average root-mean-square errors (RMSE) of the M5RT, NF-GP, and NF-SC models decreased by 11.71%, 1.68%, and 2.94%, respectively, using the LSSVR model. The applicability of the four different models was also investigated in the prediction of one-hour-ahead wind power. The results showed that NF-GP’s performance was superior to that of LSSVR, NF-SC, and M5RT. The overall average RMSEs of LSSVR, NF-SC, and M5RT decreased by 5.52%, 1.30%, and 15.6%, respectively, using NF-GP.

show abstract

Section: Least Square Support Vector Regressionmentioning

confidence: 99%

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The least squares support vector machine (LSSVM), which is a fusion of neural network and support vector machine (SVM), is an extension of SVM [ 9 ]. By using equality constraints to substitute inequality constraints and solving linear equations to obtain the support vector, one can simplify the calculation and enhance the training speed [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments

2017

View full text Add to dashboard Cite

A ship power equipments’ fault monitoring signal usually provides few samples and the data’s feature is non-linear in practical situation. This paper adopts the method of the least squares support vector machine (LSSVM) to deal with the problem of fault pattern identification in the case of small sample data. Meanwhile, in order to avoid involving a local extremum and poor convergence precision which are induced by optimizing the kernel function parameter and penalty factor of LSSVM, an improved Cuckoo Search (CS) algorithm is proposed for the purpose of parameter optimization. Based on the dynamic adaptive strategy, the newly proposed algorithm improves the recognition probability and the searching step length, which can effectively solve the problems of slow searching speed and low calculation accuracy of the CS algorithm. A benchmark example demonstrates that the CS-LSSVM algorithm can accurately and effectively identify the fault pattern types of ship power equipments.

show abstract

“…However, the neural network is easy to fall into the local optimum and has poor generalization ability. Therefore, some other scholars studied MPC method which is based on the Support Vector Machine (SVM) [7][8][9][10]. SVM has the characteristic of global optimum, strong generalization and small sample training.…”

Section: Introductionmentioning

confidence: 99%

Construction and Application of Performance Prediction Model for Aerobics Athletes Based on Online-SVM

Shao¹

2014

IJHIT

View full text Add to dashboard Cite

Aerobics has been broadly adopted in colleges and universities in our country. For aerobics can be better able to carry out, the performance prediction is very important. Aiming at the problems that the predictive model is often mismatching and difficult to solve the nonlinear optimization function of nonlinear system model of predictive control, an online support vector machine (OSVM) modeling is proposed. This proposed method builds a nonlinear model for objects using OSVM. Furthermore, we apply this method to the performance prediction of the aerobics. The results show that the online-SVM applying to the aerobics performance prediction is feasible and effective.

show abstract

A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM

Cited by 61 publications

References 5 publications

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments

Construction and Application of Performance Prediction Model for Aerobics Athletes Based on Online-SVM

Contact Info

Product

Resources

About