Aiming at the problems that existing single time series models are not accurate and robust enough when it comes to forecasting iron ore prices parameters of traditional LSSVM model difficult determine, we propose a combined based on Phase Space Reconstruction (PSR), wavelet transform (PSR-WA-LSSVM) tackle these issues. ARIMA model, LSTM PSR-LSSVM PSR-WA-LSSVM were used for contrast simulation f...

Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...

To reduce the influence of random fluctuation on wind power prediction, a new ultra-short-term prediction model, based wavelet decomposition (WD), variational mode (VMD), and least-squares support vector machine (LSSVM), is proposed in this paper. The method double LSSVM, where sequence decomposed by WD into low- high-frequency components, which are further VMD to obtain many modal components w...

In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we pro...

With the improvement of industrialization, importance equipment failure prediction is increasing day by day. Accurate gas-insulated switchgear (GIS) in advance can reduce economic loss caused power system to operate normally. Therefore, a GIS fault approach based on Improved Particle Swarm Optimization Algorithm (IPSO)-least squares support vector machine (LSSVM) proposed this paper. Firstly, f...

Predicting reservoir water levels helps manage droughts and floods. level is complex because it depends on factors such as climate parameters human intervention. Therefore, predicting needs robust models. Our study introduces a new model for levels. An extreme learning machine, the multi-kernel least square support vector machine (MKLSSVM), developed to predict of in Malaysia. The also novel op...

Side orifices are commonly installed in the side of a main channel to spill or divert some flow from source lateral channels. The aim present study is accurate estimation discharge coefficient for through triangular (Δ-shaped) by applying three data-driven models including support vector machine (SVM), least squares (LSSVM) and improved gravity search algorithm (LSSVM-GSA). was estimated utiliz...

In this paper, we apply Sequential Unconstrained Minimization Techniques (SUMTs) to the classical formulations of both the classical L1 norm SVM and the least squares SVM. We show that each can be solved as a sequence of unconstrained optimization problems with only box constraints. We propose relaxed SVM and relaxed LSSVM formulations that correspond to a single problem in the corresponding up...

A synergetic model (DWT-LSSVM) is presented in this paper. First of all, the raw data is decomposed into approximate coefficients and the detail coefficients at different scales by discrete wavelet transforms (DWT). These coefficients obtained by previous phase are then used for prediction independently using least squares support vector machines (LSSVM). Finally, these predicted coefficients a...

The accurate prediction of significant wave height (SWH) offers major safety improvements for coastal and ocean engineering applications. However, the phenomenon is nonlinear nonstationary, which makes any work a non-straightforward task. aim research presented in this paper to improve predicted via hybrid algorithm. Firstly, an empirical mode decomposition (EMD) used preprocess data, are decom...