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According to the time and space, randomness and volatility of traffic flow, a short-term traffic flow forecasting model based on empirical mode decomposition (EMD), genetic particle swarm optimization(GPSO) and support vector machine (SVM) is proposed. Firstly, the traffic flow sequence is decomposed into different frequency components by EMD. Then the crossover and mutation factors of the genetic...
This paper presents a robust and optimal operation tracking Energy Management System (EMS) for Mobile Base Transceiver Station (BTS) Microgrid equipped with Battery, PV panels and Diesel Engine Generator (DEG) for unreliable grid. The contribution is particularly focused on minimizing the DEG fuel (with DEG ON/OFF frequency) at the time of power-grid outage (i.e. Blackout). The EMS is designed to...
Prediction of stock market is a challenging task that has attracted researchers in various fields including the computational intelligence and finance. Since stock market data sets are intrinsically large, nonlinear and time-varying, it is extremely difficult to design models for forecasting the future directions with an acceptable accuracy. In this paper, an integrative and intelligent machine learning...
In the process of cost prediction modeling with support vector machine (SVM), the prediction accuracy is significantly impacted by the similarity between training samples and the predicted object. In traditional cost prediction modeling, the training data must be independent and identically distributed and every sample participating in training is treated equally. However, different samples owe the...
Presented a kind of principle and method based on regression support vector machine dynamic data significant error detection. The method takes full advantage of the nonlinear approximation capability supporting vector machine. The establishment of nonlinear system dynamic process model convex to a quadratic twice optimization problem, which can be guaranteed the extremal solution is global optimal...
As an effective method of machine learning, Support vector machine has been widely used in prediction. Proposition the supply of risk control and prevention, based on establishment evaluation index system and questionnaire to enterprise, this paper construct the supply risk prediction model and then discuss the fitting degree of model, expect to provide the basis for supply risk management.
A prediction model of heterogeneous ignition temperature of coal char particles was built based on Support Vector Machine (SVM), in which there were four input vectors, which were moisture content, ash content, volatile content and fixed carbon of coal. A new optimization approach based on microscope principle was developed when identifying the optimal parameter pair of regularization parameter ??...
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