Accurate precipitation prediction is of great significance for regional flood control and disaster mitigation. This study introduced a model based on the least square support vector machine (LSSVM) optimized by genetic algorithm (GA). The was used to estimate each meteorological station over source region Yellow River (SRYE) in China 12 months. Ensemble empirical mode decomposition (EEMD) metho...

In this present paper it deals with the Gender Classification from ECG signal using Least Square Support Vector Machine (LS-SVM) and Support Vector Machine (SVM) Techniques. The different features extracted from ECG signal using Heart Rate Variability (HRV) analysis are the input to the LS-SVM and SVM classifier and at the output the classifier, classifies whether the patient corresponding to r...

Support Vector Machines (SVMs) have become a popular classification tool. Because of their theoretical robustness they offer improvements in pattern classification applications. This paper describes an approach of producing a N-best list of hypotheses for the needs of phoneme recognition, using a Least Squares Support Vector Machine classifier (LS-SVM) and generate the corresponding N-best list...

In power generation industries, boilers are required to be operated under a range of different conditions accommodate demands for fuel randomness and energy fluctuation. Reliable prediction the combustion operation condition is crucial an in-depth understanding boiler performance maintaining high efficiency. However, it difficult establish accurate model based on traditional data-driven methods...

The present study proposes a low-energy consumption multipoint sampler carried by deep-sea landing vehicle (DSLV) to meet the requirements of time series sampling in local areas and location wide areas, an optimization method structure based on least-squares support-vector machine (LSSVM) surrogate model multi-objective particle swarm (MOPSO) algorithm. First, overall core components, such as s...