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The kernel function's selection has a great impact on the performance of support vector regression (SVR). A new method of nonlinear model predictive control (NMPC) based on polynomial kernel SVR is put forward, and multi-agent particle swarm optimization algorithm is introduced to obtain the optimal control law of rolling optimization in NMPC. Compares with the NMPC based on quadratic polynomial kernel...
In this paper, we focus on the prediction method of building energy consumption time series. The building energy consumption data can be regarded as a time series, which is usually nonlinear and non-stationary. Traditional time series analysis model has lower prediction accuracy. Then the machine learning method, especially support vector regression algorithm always has better performance to deal...
Support Vector Machine Regression (SVMR) based on ε-non-sensitive loss function is introduced to run Regression Analysis and Forecasting on the ship's hydraulic pressure field at sea. And abnormal signal detection is used to detect Ship hydraulic signal. Then we select two kernel functions for modeling and compare their detection accuracy. The calculation results show that the ε-SVR model has the...
Rolling force prediction is very important in hot bar rolling process. Aiming at the problem of predicting the bar rolling force accurately, an optimal approach of support vector regression based on improved particle swarm optimization (PSO) is proposed. A mathematic model based on the support vector regression optimized by particle swarm optimization is established, and the optimal parameter of which...
In this paper, the car sales prediction model is established by using Support Vector Regression (SVR) combined with Particles Swarm Optimization algorithm (PSO-SVR). In this model, PSO Algorithm is used to optimize the 3 parameter used in Support Vector Regression. PSO algorithm not only has a strong global search capability, but also solved the problem of over-fitting. Moreover, Mean Absolute Percentage...
Decision making under uncertainty is a critical problem in the field of software engineering. Predicting the software quality or the cost/ effort requires high level expertise. AI based predictor models, on the other hand, are useful decision making tools that learn from past projects' data. In this study, we have built an effort estimation model for a multinational bank to predict the effort prior...
Parameters selection of support vector machine is the key issue that impacts its accurate performance. A method for support vector regression machine with basic particle swarm optimization (BPSO) algorithm is proposed in this paper. Furthermore, in order to improve the efficiency of the PSO algorithm, a linear decreasing strategy is used to dynamically change the weight. So, an improve PSO (IPSO)...
In this paper the wind speed forecasting in a wind farm, applying the algorithm of support vector regression (SVR) to the mean 10-minute time series is presented. By comparing its performance with an back propagation neural network model through simulation results, we could find following facts: firstly, both algorithms are applicable for prediction the wind speed time series in future; secondly,...
Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw data from Taiwan...
Support vector regression (SVR) is proved to be a good and effective method for machine condition prediction. But prediction results are usually not satisfying for complex machines, e.g., a certain diesel engine. So a hybrid method GA-EMD-SVR is proposed in this paper integrating genetic algorithm (GA), empirical mode decomposition (EMD) and support vector regression (SVR). The main ideal of the method...
Prediction on complex time series has received much attention during the last decades. Global model is the main tool for time series predicting during the last decades, but it suffers low prediction efficiency, low prediction accuracy and high computation complexity for model training and updating. In recent years, local model for time series prediction draws widely attention for its more accuracy...
In this paper, we present a new approach for very short term electricity load demand forecasting. In particular, we apply support vector regression to predict the load demand every 5 minutes based on historical data from the Australian electricity operator NEMMCO for 2006-2008. The results show that support vector regression is a very promising approach, outperforming backpropagation neural networks,...
SVM which is based on statistical theory has the advantage of no relying on designer's experience of learning and the prior knowledge. So it is widely used in optimization, decision-making, regression estimates, speech recognition, facial image recognition, and so on. Because there are some kinds of wrong and isolated samples in the training samples in the forecasting model, and the learning process...
A number of different forecasting methods have been proposed for traffic flow forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, a new short-term...
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