Particle swarm optimization-based least squares support vector regression for critical heat flux prediction

System reliability analysis of slopes using least squares support vector machines with particle swarm optimization

“…As a result, LSSVM expresses model training in terms of solving a linear system instead of a quadratic programming problem, which thus reduces the computation time significantly. The LSSVM model showed the excellent performance in the various prediction fields, such as discharge-suspended sediment relationship modeling [25], heat flux prediction [26], seismic reliability assessment of structures [27], etc. In recent years, LSSVM gained more and more popularity for geotechnical forecasting [21,28].…”

Robust data-driven soft sensor based on iteratively weighted least squares support vector regression optimized by the cuckoo optimization algorithm

“…Therefore, some optimization algorithms were put forward. For instance, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are two widely used optimization algorithms that can be used for the LSSVR parameter selection (Yang et al, 2010;Jiang and Zhao, 2013). Another problem of LSSVR that should be considered is its robustness ability.…”

A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting

“…The efficiency of PSO-LSSVM has been demonstrated by [4] in critical heat flux prediction (CHF). Applied in nuclear science field, accurate prediction of CHF is vital not only for safety but also in designing nuclear reactors.…”

“…Unlike the previous study of PSO-LSSVM [4,40], in [42], the dynamic inertia weight PSO (WPSO) is employed to optimize LSSVM hyper-parameters. The optimal value of parameters obtained is then used by LSSVM for prediction task.…”

“…LSSVM has been recognized as one of the tool in solving various data mining tasks which include prediction, classification and many others [3].Nonetheless, besides its diversity in application, the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter, γ and kernel parameter, σ 2 [4].…”