Nanchang Institute of Technology is a university in Nanchang, Jiangxi, China.Nanchang Institute of Technology is located in the capital city of Jiangxi province - Nanchang, is approved by the Ministry of Education set up a second level of regular undergraduate institutions, a bachelor's degree-granting units. According to April 2014 the school's official website revealed that the campus covers an area of 757.79 acres. 1.19 million square meters have been built school, library books 2,650,000, a variety of educational assets totaled 1.8 billion rmb, teaching equipment worth 176 million rmb. The school has 16 sub-colleges, 42 undergraduate majors, 62 specialties, Jiangxi university "five" key disciplines 2 , there are students in nearly 30,000 people . Wikipedia.
Che J.,Nanchang Institute of Technology |
Wang J.,Lanzhou University |
Wang G.,East China Jiaotong University |
Wang G.,Baoshan College
Energy | Year: 2012
Electric load forecasting is an important task in the daily operations of a power utility associated with energy transfer scheduling, unit commitment and load dispatch. Inspired by the various non-linearity of electric load data and the strong learning capacity of support vector regression (SVR) for small sample and balanced data, this paper presents an adaptive fuzzy combination model based on the self-organizing map (SOM), the SVR and the fuzzy inference method. The adaptive fuzzy combination model can effectively count for electric load forecasting with good accuracy and interpretability at the same time. The key idea behind the combination is to build a human-understandable knowledge base by constructing a fuzzy membership function for each homogeneous sub-population. The comparison of different mathematical models and the effectiveness of the presented model are shown by the real data of New South Wales electricity market. The obtained results confirm the validity of the developed model. © 2011 Elsevier Ltd.
Li L.,Nanchang Institute of Technology
International Journal of Advancements in Computing Technology | Year: 2012
This paper proposes a vector learning particle swarm optimization based on the traditional particle swarm optimization (PSO) algorithm. Unlike the traditional PSO algorithm, the algorithm does not update the entire speed or position vector at the same time, but divides the entire speed or position vector into sub-vectors and cyclically updates each speed or position sub-vector in order. By introducing a new speed update learning strategy in speed sub-vector update, the algorithm makes the particles examine ahead the effects of particle evolution and feed the information back during the update process, to finally determine the next generation value of the particle speed sub-vector, which can effectively avoid the blindness of speed evolution. On the other hand, the speed update learning strategy contains a diversity factor that can effectively expand the diversity of particle evolution. The experimental results show that: This algorithm has strong global search ability for most standard composite test functions and stronger optimization ability than popular PSO based improved algorithms and intelligent single particle optimizer.
Wang H.,Nanchang Institute of Technology
Mathematical Problems in Engineering | Year: 2012
This paper presents a modified barebones particle swarm optimization (OBPSO) to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization (BPSO) and opposition-based learning (OBL) to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. Moreover, an adaptive penalty method is employed to handle constraints. To verify the performance of OBPSO, a set of well-known constrained benchmark functions is used in the experiments. Simulation results show that our approach achieves a promising performance. © 2012 Hui Wang.
Liu Y.,Nanchang Institute of Technology
Procedia Engineering | Year: 2012
Based on Karhunen-Loève expansion of random field, the paper utilizes Hermite polynomial chaos expansion to build the stochastic response surface function, and the unknown coefficients of the function can be calculated by probabilistic collocation approach. Then, the geometric method can be used to calculate the structural reliability. A comparison with Monte Carlo simulation shows that the proposed method can achieve high accuracy and efficiency while analyzing the property of the second-order and the third-order stochastic response surface. © 2012 Published by Elsevier Ltd.
Che J.X.,Nanchang Institute of Technology
Applied Soft Computing Journal | Year: 2013
Support vector regression (SVR) has become very promising and popular in the field of machine learning due to its attractive features and profound empirical performance for small sample, nonlinearity and high dimensional data application. However, most existing support vector regression learning algorithms are limited to the parameters selection and slow learning for large sample. This paper considers an adaptive particle swarm optimization (APSO) algorithm for the parameters selection of support vector regression model. In order to accelerate its training process while keeping high accurate forecasting in each parameters selection step of APSO iteration, an optimal training subset (OTS) method is carried out to choose the representation data points of the full training data set. Furthermore, the optimal parameters setting of SVR and the optimal size of OTS are studied preliminary. Experimental results of an UCI data set and electric load forecasting in New South Wales show that the proposed model is effective and produces better generalization performance. © 2013 Elsevier B.V. All rights reserved.