An Approximate Solution for a Simple Pendulum beyond the Small Angles Regimes Using Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm

被引:1
|
作者
Yekrangi, Alireza [2 ]
Ghalambaz, Mehdi [3 ]
Noghrehabadi, Aminreza [1 ]
Beni, Yaghoub Tadi [4 ]
Abadyan, Mohamadreza [2 ]
Abadi, Molood Noghreh [5 ]
Abadi, Mehdi Noghreh [6 ]
机构
[1] Islamic Azad Univ, Ahvaz Branch, Dept Mech Engn, Ahvaz, Iran
[2] Islamic Azad Univ, Ramsar Branch, Mech Engn Grp, Ramsar, Iran
[3] Islamic Azad Univ, Dezful Brach, Dept Engn Mech, Dezful, Iran
[4] Univ Shahrekord, Fac Engn, Shahrekord, Iran
[5] Islamic Azad Univ, Dezful Branch, Dept Comp Engn, Dezful, Iran
[6] Islamic Azad Univ, Khouzestan Sci & Res Branch, Ahvaz, Iran
关键词
Simple pendulum; Differential equation; Neural networks; Particle Swarm Optimization (PSO);
D O I
10.1016/j.proeng.2011.04.611
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Simple pendulum is the most popular example in mechanics. Study on the physics of simple pendulum is a key to understanding the nonlinear dynamics of many other systems. However, there is an exact analytical solution for this problem, but its exact solution is in the form of the Jacobi elliptic integral which it is hard for using in simple engineering manipulations. Hence, determining an accurate simple approximate solution is helpful. This study presents a new method by using hybrid neural networks and particle swarm optimization algorithm, in order to find a simple approximate solution for motion of a nonlinear pendulum beyond the small angles regime. The approximate solution is simple and powerful to converge to the exact solution. The results of the approximate solution are compared with exact solution and linear solution, using tables and graphs. Furthermore, the present method is expandable to solve complex pendulums. (C) 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of ICM11
引用
收藏
页码:3734 / 3740
页数:7
相关论文
共 50 条
  • [21] Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm
    Katherasan, D.
    Elias, Jiju V.
    Sathiya, P.
    Haq, A. Noorul
    JOURNAL OF INTELLIGENT MANUFACTURING, 2014, 25 (01) : 67 - 76
  • [22] Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm
    D. Katherasan
    Jiju V. Elias
    P. Sathiya
    A. Noorul Haq
    Journal of Intelligent Manufacturing, 2014, 25 : 67 - 76
  • [23] Estimation of Number of Flight Using Particle Swarm Optimization and Artificial Neural Network
    Ozmen, Ebru Pekel
    Pekel, Engin
    ADCAIJ-ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL, 2019, 8 (03): : 27 - 33
  • [24] Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization
    Lazzus, Juan A.
    INTERNATIONAL JOURNAL OF THERMOPHYSICS, 2011, 32 (05) : 957 - 973
  • [25] Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization
    Juan A. Lazzús
    International Journal of Thermophysics, 2011, 32
  • [26] Optimizing Artificial Neural Network for Functions Approximation Using Particle Swarm Optimization
    Zaghloul, Lina
    Zaghloul, Rawan
    Hamdan, Mohammad
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2021, PT I, 2021, 12689 : 223 - 231
  • [27] Compressive Strength Prediction of Rubber Concrete Based on Artificial Neural Network Model with Hybrid Particle Swarm Optimization Algorithm
    Huang, Xiao-Yu
    Wu, Ke-Yang
    Wang, Shuai
    Lu, Tong
    Lu, Ying-Fa
    Deng, Wei-Chao
    Li, Hou-Min
    MATERIALS, 2022, 15 (11)
  • [28] Identification of realistic distillation column using hybrid particle swarm optimization and NARX based artificial neural network
    Jaleel, E. Abdul
    Aparna, K.
    EVOLVING SYSTEMS, 2019, 10 (02) : 149 - 166
  • [29] Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization
    Hajihassani, M.
    Armaghani, D. Jahed
    Sohaei, H.
    Mohamad, E. Tonnizam
    Marto, A.
    APPLIED ACOUSTICS, 2014, 80 : 57 - 67
  • [30] Identification of realistic distillation column using hybrid particle swarm optimization and NARX based artificial neural network
    E. Abdul Jaleel
    K. Aparna
    Evolving Systems, 2019, 10 : 149 - 166