Parameters estimation of VG equation based on fitting method and particle swarm optimization

Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (11): 58-60.

Previous Articles Next Articles

Parameters estimation of VG equation based on fitting method and particle swarm optimization

CAO Huaihuo1, OUYANG Aijia2, AI Hainan3

1.Department of Mathematics and Computer Science, Chizhou College, Chizhou, Anhui 247000, China
2.School of Computer and Communication, Hunan University, Changsha 410082, China
3.Laboratory of Three Gorges Reservoir Region’s Eco-Environment, Ministry of Ducation, Chongqing University, Chongqing 400045, China

Online:2013-06-01 Published:2013-06-14

基于拟合与粒子群优化的VG方程参数估计

曹怀火1，欧阳艾嘉2，艾海男3

1.池州学院数学与计算机科学系，安徽池州 247000
2.湖南大学计算机与通信学院，长沙 410082
3.重庆大学三峡库区生态环境教育部重点实验室，重庆 400045

Abstract

Abstract: Taking into the particle swarm optimization is affected by the initial values and produces easily the local optimal solutions, based on lsqcurvefit fitted method and particle swarm optimization, a new hybrid algorithm of particle swarm optimization is proposed to estimate the parameters for Van Genuchtem equation and get better results. The result of a case study shows the algorithm is characterized with high accuracy, fast convergence and high robustness in the parameters estimation, moreover, it does not need the initial parameter value. Therefore, this method should be promoted.

Key words: lsqcurvefit fitting method, particle swarm optimization, Van Genuchten equation, parameters estimation

摘要： 考虑到粒子群算法受初值影响，易于产生局部最优解的缺陷，将lsqcurvefit拟合方法与粒子群算法相结合，提出一种新的混合型粒子群优化算法，用于Van Genuchten方程参数估计得到了较好的结果。数值实验结果分析表明，该算法在参数估计中求解精度高、收敛速度快、寻优能力强，而且不需要给出参数的初始值，是一种值得推广的方法。

关键词: lqcurvefit拟合方法, 粒子群算法, Van Genuchten方程, 参数估计

CAO Huaihuo1, OUYANG Aijia2, AI Hainan3. Parameters estimation of VG equation based on fitting method and particle swarm optimization[J]. Computer Engineering and Applications, 2013, 49(11): 58-60.

曹怀火1，欧阳艾嘉2，艾海男3. 基于拟合与粒子群优化的VG方程参数估计[J]. 计算机工程与应用, 2013, 49(11): 58-60.

[1]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[2]	ZHU Jiaying, GAO Maoting. AUV Path Planning Based on Particle Swarm Optimization and Improved Ant Colony Optimization [J]. Computer Engineering and Applications, 2021, 57(6): 267-273.
[3]	LI Jingxing, YANG Youlong. Feature Selection of Markov Blanket for High Dimensional Data [J]. Computer Engineering and Applications, 2021, 57(6): 58-66.
[4]	HU Xiaomin, WANG Mingfeng, ZHANG Shourong, LI Min. New Differential Evolution with Particle Swarm Optimization Algorithm for Text Clustering [J]. Computer Engineering and Applications, 2021, 57(4): 61-67.
[5]	WANG Jiayue, SHI Li. Research on Path Optimization and Charging Strategy of Electric Refrigerated Vehicle Considering Cost of Battery Capacity Degradation [J]. Computer Engineering and Applications, 2021, 57(24): 283-289.
[6]	CHEN Wanfen, WANG Yujia, LIN Weixing. Heterogeneous Ensemble Surrogate Assisted Multi-objective Particle Swarm Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 71-80.
[7]	DU Shouxin, WU Tao. Study on Application of Double Population Hybrid Genetic Algorithm in Cut Order Planning [J]. Computer Engineering and Applications, 2021, 57(22): 182-189.
[8]	LIN Weixing, WANG Yujia, CHEN Wanfen, LIANG Haina. High-Dimensional Data Feature Selection Algorithm Based on Multifactor Particle Swarm Optimization [J]. Computer Engineering and Applications, 2021, 57(22): 199-207.
[9]	LI Leixiao, DENG Dan, LI Jie, WANG Yongsheng. All-to-All Comparison Computing Data Distribution Strategy Based on Particle Swarm Optimization [J]. Computer Engineering and Applications, 2021, 57(15): 109-117.
[10]	YU Guolong, ZHAO Yong, WU Lian, CUI Zhongwei. Improvement of QPSO Algorithm and Its Application in DBN Parameter Optimization [J]. Computer Engineering and Applications, 2021, 57(10): 154-162.
[11]	LIAO Weilin, CHENG Shan, SHANG Dongdong, WEI Zhaobin. Particle Swarm Optimization Algorithm Integrated with Multiple-Strategies [J]. Computer Engineering and Applications, 2021, 57(1): 69-76.
[12]	ZHOU Rong, LI Jun, WANG Hao. Reverse Learning Particle Swarm Optimization Based on Grey Wolf Optimization [J]. Computer Engineering and Applications, 2020, 56(7): 48-56.
[13]	HUO Lin, LU Yinli. Improved Particle Swarm Optimization for Android Malware Detection [J]. Computer Engineering and Applications, 2020, 56(7): 96-101.
[14]	CHEN Qiuju, XU Jianguo. Optimized Orthogonal Matching Pursuit and Short-Time Spectrum Estimation for Sound Recognition [J]. Computer Engineering and Applications, 2020, 56(7): 162-169.
[15]	YANG Hualong, LU Ting, XIN Yuchen. Improved Particle Swarm Optimization for Two-Echelon IRP with Heterogeneous Fleet [J]. Computer Engineering and Applications, 2020, 56(22): 272-278.

Parameters estimation of VG equation based on fitting method and particle swarm optimization

基于拟合与粒子群优化的VG方程参数估计

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics