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Xuemei L.,South China University of Technology | Xuemei L.,Zhongkai University of Agriculture and Engineering | Ming S.,South China University of Technology | Lixing D.,Zhongkai University of Agriculture and Engineering | And 3 more authors.
Journal of Computers | Year: 2010

Accurate predicting of building cooling load has been one of the most important issues in the energy-saving building, which provides an approach to integrate and optimize the heating, ventilating, and air-conditioning (HVAC) system cooling supply system efficiently. Because of the remarkable nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting building cooling load, but suffer from the phenomena of local minimum and over-fitting. This paper investigates the feasibility of using Least Squares Support vector regression (LS-SVR) to forecast building cooling load. LS-SVR is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. Due to the importance of parameters optimization in LS-SVR model, particle swarm optimization (PSO) was used to optimize the model parameters. The experiment results show that PSO can quickly obtain the optimal parameters satisfying the precision requirement with a simple calculation, which solves the problem of complex calculation and empiricism in conventional methods. The evaluation on the testing cases shows the SVR model with PSO has a good generalization performance and can be a promising alternative for building cooling load prediction. © 2010 Academy Publisher. Source

Pan M.,South China University of Technology | Zeng D.,South China University of Technology | Zeng D.,Shenzhen University | Xu G.,Shenzhen University | And 2 more authors.
Lecture Notes in Electrical Engineering | Year: 2010

Hydrogen gas concentration forecasting and evaluation is very important for Bio-ethanol Steam Reforming hydrogen production. A lot of methods have been applied in the field of gas concentration forecasting including principal component analysis (PCA) and artificial neural network (ANN) etc. this paper used kernel principal component analysis (KPCA) as a preprocessor of Least Squares Support Vector Machine (LS-SVM) to extract the principal features of original data and employed the Particle Swarm Optimization (PSO) to optimize the free parameters of LS-SVM. Then LS-SVM is applied to proceed hydrogen gas concentration regression modeling. The experiment results show that KPCA-LSSVM features high learning speed, good approximation and generalization ability compared with SVM and PCA-SVM. © 2010 Springer-Verlag Berlin Heidelberg. Source

Li X.,Zhongkai University of Agriculture and Engineering | Chen J.,Zhongkai University of Agriculture and Engineering | Lv J.,Zhongkai University of Agriculture and Engineering | Xu G.,Shenzhen Key Laboratory of Mould Advanced Manufacture
OPEE 2010 - 2010 International Conference on Optics, Photonics and Energy Engineering | Year: 2010

Mobile robotics has been used widely in education as a learning tool, as it provides a motivating and interesting tool to perform laboratory experiments within the context of mechatronics, electronics, computer, and control. The robot innovative platform made it possible for the students to practice and learn many necessary skills. A tracing algorithm about machine vision intelligent tracing System based on CCD camera was described. The proposed system is comprised of a modular robot and the microcontroller XS128 as tools to design and implement the intelligent tracing system. The objective of this machine vision system is to implement an intelligent tracing system, which can track a black line as fast and stable as possible. It is student-oriented and career-oriented. The encouraging results demonstrate that students learn to design and give creative solution to given problems from an interdisciplinary point of view. © 2010 IEEE. Source

Minqiang P.,South China University of Technology | Dehuai Z.,South China University of Technology | Dehuai Z.,Shenzhen University | Gang X.,Shenzhen University | Gang X.,Shenzhen Key Laboratory of Mould Advanced Manufacture
Journal of Computers | Year: 2010

Temperature forecasting of hydrogen-producing reactor is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. The determination for hyper-parameters including kernel parameters and the regularization is important to the performance of SVM. Particle Swarm Optimization (PSO) is a method for finding a solution of stochastic global optimizer based on swarm intelligence. Using the interaction of particles, PSO searches the solution space intelligently and finds out the best one. Thus, the proposed forecasting model based on the global optimization of PSO and local accurate searching of SVM is applied to forecast hydrogen-producing reactor temperature in this paper. Practical example results indicate that the application of the PSO-SVM method to temperature forecasting of hydrogen-producing reactor is feasible and effective. And to prove the effectiveness of the model, other existing methods are used to compare with the result of SVM. The results show that the model is effective and highly accurate in the forecasting of hydrogen-producing reactor temperature. © 2010 ACADEMY PUBLISHER. Source

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