Problem statement: As the performance of Least Squares Support Vector Machines (LSSVM) is highly rely on its value of regularization parameter, γ and kernel parameter, σ, manmade approach is clearly not an appropriate solution since it may lead to blindness in certain extent. In addition, this technique is time consuming and unsystematic, which consequently affect the generalization performance...

Anomaly detection is the identification of abnormal system behavior, in which a model of normality is constructed, with deviations from the model identified as “abnormal”. Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Anomaly detection is partic...

Research of short-term load forecasting has important practical application value in the field of power network dispatching. The regession models of least squares support vector machines (LS-SVM) have been applied to load forecasting field widely, and the regression accuracy and generalization performance of the LS-SVM models depend on a proper selection of its parameters. In this paper, a new ...

Purpose The ability to predict cash demand is crucial for the operation of construction companies. Reliable cash flow prediction during the execution phase can help managers to avoid cash shortages and to control project cash flow effectively. Method This paper presents a new inference model, CF-ELSIMT, for cash flow forecasting. The developed CF-ELSIMT utilizes weighted Least Squares Support V...

How to analyze the features of stock price accurately and master the regularity of stock price changing with time quickly and effectively is of great theoretical and realistic significance and is an important research direction in financial field. For complicated non-linear and periodic variations of stock prices, a parallel computing model is proposed in this paper based on stock prediction al...

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. And analytic hierarchy process (AHP) is used to sieve out the more influential factors. Combined w...

In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support vector machine (LSSVM). With the GMDH is used to determine the inputs of LSSVM method and the LSSVM model which works as time series forecasting. The aim of this study is t...

This paper presents an optimization algorithm to solve the short-term load forecasting problem more quickly and accurately in progress of smart grid development. The new approach employs generalized regression neural network (GRNN) to select influence factors of short-term load, and then a least squares-support vector machine (LS-SVM) based on harmony search algorithm (HS) optimization algorith...

In order to achieve continuous navigation capability in areas such as tunnels, urban canyons, and indoors a new approach using least squares support vector machine LS-SVM and H∞ filter HF for integration of INS/WSN is proposed. In the integrated system, HF estimates the errors of position and velocity while the signals in WSNs are available. Meanwhile, the compensation model is trained by LS-SV...

In this paper new robust methods for tuning regularization parameters or other tuning parameters of a learning process for non-linear function estimation are proposed: repeated robust cross-validation score functions (repeated-CV Robust V −fold) and a robust generalized cross-validation score function (GCVRobust). Both methods are effective for dealing with outliers and non-Gaussian noise distr...