In order to effectively solve the problems of low prediction accuracy and calculation efficiency existing methods for estimating economic loss in a subway station engineering project due rainstorm flooding, new intelligent model is developed using sparrow search algorithm (SSA), least-squares support vector machine (LSSVM) mean impact value (MIV) method. First, this study, 11 input variables ar...

Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of prediction lies in multi-factor multi-effect nature open-pit blasting. This study proposes a hybrid intelligent model predict vibrations using least-squares support vector machine (LSSVM) optimized by particle swarm algorithm (PSO). Meanwhile, multi...

Fault diagnosis is a challenging topic for complex industrial systems due to the varying environments such find themselves in. In order improve performance of fault diagnosis, this study designs novel approach by using particle swarm optimization (PSO) with wavelet mutation and least square support (LSSVM). The implementation entails following three steps. Firstly, original signals are decompos...

Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not ful...

Fixed-Size Least Squares Support Vector Machines (FS-LSSVM) is a powerful tool for solving large scale classification and regression problems. FS-LSSVM solves an over-determined system of M linear equations by using Nyström approximations on a set of prototype vectors (PVs) in the primal. This introduces sparsity in the model along with ability to scale for large datasets. But there exists no f...

Predicting the price of electricity is crucial for operation power systems. Short-term forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead offer expected prices market participants before hours. This especially useful effective bidding strategies where amount can be reviewed or changed Nevertheless, many existing models have relatively low prediction accuracy. Furthermore...

Least squares support vector machine (LSSVM) is a learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, it has good performance in the data processingof small samples. The LSSVM model lacks sparsity unable to handle large-scale problem, this articleproposes an method mixture kernel sparse This reduces theinitial training set sub-dataset usi...

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsp...

As a key hydrological parameter, daily reference evapotranspiration (ETo) determines the accuracy of the hydrological number of the crop, and, consequently, the regional optimization disposition of water resources. At present, the main methods for ETo estimation are the Penman-Monteith (PM) equation and its modified formula, both of which are based on climatic factors such as temperature, radia...