Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
article

Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm

Published: 01 June 2018 Publication History

Abstract

Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation (BP) neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm (CGA) is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the realtime performance and accuracy of the gesture recognition are greatly improved with CGA.

References

[1]
Z. X. Huang, B. Peng, J. Wu. Research and application of human-computer interaction system based on gesture recognition technology. In Proceedings of International Conference on Computer and Computing Technologies in Agriculture, Beijing, China, pp. 210-215, 2012.
[2]
C. N. Song. Android-based remote-control with real-time video surveillance for Wi-Fi robot. In Proceedings of International Conference on Trustworthy Computing and Services, Beijing, China, pp. 382-388, 2013.
[3]
C. Lee, M. Kim, J. Park, J. Oh, K. Eom. Development of wireless RFID glove for various applications. Security-Enriched Urban Computing and Smart Grid, T. H. Kim, A. Stoica, R. S. Chang, Eds. Berlin, Heidelberg, Germany: Springer, pp. 292-298, 2010.
[4]
S. S. Rautaray, A. Agrawal. Vision based hand gesture recognition for human computer interaction: A survey. Artificial Intelligence Review, vol. 43, no. 1, pp. 1-54, 2015.
[5]
N. Zhang, S. Ding, J. Zhang, Y. Xue. Research on pointwise gated deep networks. Applied Soft Computing, vol. 52, pp. 1210-1221, 2017.
[6]
L. Jiang, Q. Q. Ruan. Research of gesture recognition based on neuron networks. Journal of Beijing Jiaotong University, vol. 30, no. 5, pp. 32-36, 2006. (In Chinese).
[7]
L. S. Xiang, F. Qi, X. Y. Liu. A new optimization method for neural tree network model. Control and Decision, vol. 28, no. 1, pp. 73-77, 2013. (In Chinese).
[8]
X. Q. Zeng. Study on the parameter optimization problem of BP neural network in the modeling. Meteorological Monthly, vol. 39, no. 3, pp. 333-339, 2013. (In Chinese).
[9]
L. H. You, J. J. Wu, Y. Wang, S. J. Song. Optimized BP neural network based on simulated annealing algorithm for pH value prediction. Chinese Journal of Sensors and Actuators, vol. 27, no. 12, pp. 1643-1648, 2014. (In Chinese).
[10]
Z. Salim. New time-varying fuzzy sets based on a PSO midpoint of the universe of discourse. International Journal of Automation and Computing, vol. 13, no. 4, pp. 392-400, 2016.
[11]
S. N. Pawar, R. S. Bichkar. Genetic algorithm with variable length chromosomes for network intrusion detection. International Journal of Automation and Computing, vol. 12, no. 3, pp. 337-342, 2015.
[12]
E. A. Dil, M. Ghaedi, A. Asfaram, F. Mehrabi, A. A. Bazrafshan, A. M. Ghaedi. Trace determination of safranin O dye using ultrasound assisted dispersive solid-phase micro extraction: Artificial neural network-genetic algorithm and response surface methodology. Ultrasonics Sonochemistry, vol. 33, pp. 129-140, 2016.
[13]
C. A. C. Coello, E. M. Montes. Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Advanced Engineering Informatics, vol. 16, no. 3, pp. 193-203, 2002.
[14]
J. W. Peng, W. H. Lv, H. Y. Xing, X. J. Wu. Temperature compensation for humidity sensor based on improved GABP neural network. Chinese Journal of Scientific Instrument, vol. 34, no. 1, pp. 153-160, 2013. (In Chinese).
[15]
L. Q. Xiao, H. X. Wang, H. L. Cheng, X. J. Xu. Topology optimization of ERT finite element model based on improved GA. Chinese Journal of Scientific Instrument, vol. 33, no. 7, pp. 1490-1496, 2012. (In Chinese).
[16]
Q. Li, J. Gong, J. F. Tang. Multi-objective particle swarm optimization algorithm for cross-training programming. Control Theory & Applications, vol. 30, no. 1, pp. 18-22, 2013. (In Chinese).
[17]
M. Islam, M. R. Rana, T. Rahman, M. Shahjahan. A biologically plausible neural network training algorithm with composite chaos. In Proceedings of the 15th International Conference on Computer and Information Technology, IEEE, Chittagong, Bengal, pp. 15-20, 2012.
[18]
L. G. Chen, H. D. Chiang, N. Dong, R. P. Liu. Group-based chaos genetic algorithm and non-linear ensemble of neural networks for short-term load forecasting. IET Generation, Transmission & Distribution, vol. 10, no. 6, pp. 1440-1447, 2016.
[19]
A. Rasoolzadeh, M. S. Tavazoei. Prediction of chaos in non-salient permanent-magnet synchronous machines. Physics Letters A, vol. 377, no. 1-2, pp. 73-79, 2012.
[20]
S. Farzin, P. Ifaei, N. Farzin, Y. Hassanzadeh, M. T. Aalami. An investigation on changes and prediction of Urmia Lake water surface evaporation by chaos theory. International Journal of Environmental Research, vol. 6, no. 3, pp. 815-824, 2012.
[21]
L. S. Yin, Q. Jiang, Q. Z. Hu. Research on wavelet neural network traffic flow model and rediction based on chaos algorithm. Chinese Journal of Scientific Instrument, vol. 30, no. 6, pp. 405-409, 2009. (In Chinese).
[22]
K. R. Nirmal, N. Mishra. 3D graphical user interface on personal computer using p5 Data Glove. International Journal of Computer Science Issues, vol. 8, no. 5, pp. 155-160, 2011.
[23]
Y. J. Wang, X. D. Wang, Y. Q. Zhou, Y. X. Yan. GA-BP network based battery SOC prediction for quasi anti-damage power supply. Electric Machines and Control, vol. 14, no. 6, pp. 61-65, 2010. (In Chinese).
[24]
J. F. Yao, C. Mei, X. Q. Peng. The application research of the chaos genetic algorithm (CGA) and its evaluation of optimization efficiency. Acta Automatica Sinica, vol. 28, no. 6, pp. 935-942, 2002. (In Chinese).
[25]
N. Tosun, L. Özler. A study of tool life in hot machining using artificial neural networks and regression analysis method. Journal of Materials Processing Technology, vol. 124, no. 1-2, pp. 99-104, 2002.
[26]
I. Benacer, Z. Dibi. Extracting parameters of OFET before and after threshold voltage using genetic algorithms. International Journal of Automation and Computing, vol. 13, no. 4, pp. 382-391, 2016.
[27]
L. Mourelle, R. E. Ferreira, N. Nedjah. Migration selection of strategies for parallel genetic algorithms: Implementation on networks on chips. International Journal of Electronics, vol. 97, no. 10, pp. 1227-1240, 2010.
[28]
X. P. Chen, S. L. Yu. Improvement on crossover strategy of real-valued genetic algorithm. Acta Electronica Sinica, vol. 31, no. 1, pp. 71-74, 2003. (In Chinese).
[29]
A. Avancini, M. Ceccato. Comparison and integration of genetic algorithms and dynamic symbolic execution for security testing of cross-site scripting vulnerabilities. Information and Software Technology, vol. 55, no. 12, pp. 2209-2222, 2013.
[30]
Y. Q. Huang, C. Y. Liang, S. L. Yang, Q. Lu. Interactive genetic algorithm based on accelerating convergent mutation strategy. Journal of System Simulation, vol. 19, no. 9, pp. 1913-1916, 2007. (In Chinese).
[31]
C. M. Fernandes, J. L. J. Laredo, A. M. Mora, A. C. Rosa, J. J. Merelo. The sandpile mutation operator for genetic algorithms. In Proceedings of International Conference on Learning and Intelligent Optimization, Rome, Italy, pp. 552-556, 2011.
[32]
D. P. Tian, T. X. Zhao. Particle swarm optimization based on Tent map and Logistic map. Journal of Shaanxi University of Science & Technology, vol. 28, no. 2, pp. 17-23, 2010. (In Chinese).
[33]
M. W. Fagerland, D. W. Hosmer. Tests for goodness of fit in ordinal logistic regression models. Journal of Statistical Computation and Simulation, vol. 86, no. 17, pp. 3398-3418, 2016.
[34]
M. Javidi, R. Hosseinpourfard. Chaos genetic algorithm instead genetic algorithm. The International Arab Journal of Information Technology, vol. 12, no. 2, pp. 163-168, 2015.

Cited By

View all
  • (2024)GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature CompensationInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.33749119:1(1-14)Online publication date: 21-Feb-2024
  • (2024)Machine Learning and Natural Language Processing Algorithms in the Remote Mobile Medical Diagnosis System of Internet HospitalsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/363217223:6(1-17)Online publication date: 22-Jun-2024
  • (2023)Analyzing the Relationship among Social Capital, Dynamic Capability, and Farmers' Cooperative Performance Using Lightweight Deep Learning ModelComputational Intelligence and Neuroscience10.1155/2023/70642362023Online publication date: 1-Jan-2023
  • Show More Cited By
  1. Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image International Journal of Automation and Computing
      International Journal of Automation and Computing  Volume 15, Issue 3
      June 2018
      128 pages

      Publisher

      Springer-Verlag

      Berlin, Heidelberg

      Publication History

      Published: 01 June 2018

      Author Tags

      1. Gesture recognition
      2. back propagation (BP) neural network
      3. chaos algorithm
      4. data glove
      5. genetic algorithm

      Qualifiers

      • Article

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 17 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature CompensationInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.33749119:1(1-14)Online publication date: 21-Feb-2024
      • (2024)Machine Learning and Natural Language Processing Algorithms in the Remote Mobile Medical Diagnosis System of Internet HospitalsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/363217223:6(1-17)Online publication date: 22-Jun-2024
      • (2023)Analyzing the Relationship among Social Capital, Dynamic Capability, and Farmers' Cooperative Performance Using Lightweight Deep Learning ModelComputational Intelligence and Neuroscience10.1155/2023/70642362023Online publication date: 1-Jan-2023
      • (2023)Fault diagnosis of Marine power station based on optimized GA-BPProceedings of the 2023 4th International Conference on Machine Learning and Computer Application10.1145/3650215.3650229(63-68)Online publication date: 27-Oct-2023
      • (2022)Application of Three-Stage DEA Model Combined with BP Neural Network in Microfinancial Efficiency EvaluationComputational Intelligence and Neuroscience10.1155/2022/85006622022Online publication date: 1-Jan-2022
      • (2022)Innovative College Students’ Educational Management Mode Based on BP Neural NetworkWireless Communications & Mobile Computing10.1155/2022/75033152022Online publication date: 1-Jan-2022
      • (2022)An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning AlgorithmScientific Programming10.1155/2022/51590272022Online publication date: 1-Jan-2022
      • (2022)Parameter Optimization and State Evaluation of Basketball Teaching Based on BPNNMobile Information Systems10.1155/2022/43273562022Online publication date: 1-Jan-2022
      • (2022)Evaluating the Vocal Music Teaching Using Backpropagation Neural NetworkMobile Information Systems10.1155/2022/38437262022Online publication date: 1-Jan-2022
      • (2022)Sales Forecast of Marketing Brand Based on BP Neural Network ModelComputational Intelligence and Neuroscience10.1155/2022/17694242022Online publication date: 1-Jan-2022
      • Show More Cited By

      View Options

      View options

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media