DOI QR코드

DOI QR Code

A Study on Static Situation Awareness System with the Aid of Optimized Polynomial Radial Basis Function Neural Networks

최적화된 pRBF 뉴럴 네트워크에 의한 정적 상황 인지 시스템에 관한 연구

  • Received : 2011.09.19
  • Accepted : 2011.10.25
  • Published : 2011.12.01

Abstract

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

Keywords

Radial basis function neural network;Fuzzy C means clustering;K-means clustering;Particle swarm optimization;NXT;Situation awareness

References

  1. A. A. Frolov, D. Husek, I. P. Muraviev, P. Y. Polyakov, "A Boolean Factor Analysis by Attractor Neural Network," IEEE Trans. Neural Networks, Vol. 3, pp. 698-707, 2007.
  2. R. A. Aliev, B .G. Guirimov, B. Fazlollahi, and R. R. Aliev "Evolutionary Algorithm-based Learning of Fuzzy Neural Networks. part 2: Recurrent Fuzzy Neural Networks," Fuzzy Sets and Systems, Vol. 160, No. 17, pp. 2553-2566, 2009. https://doi.org/10.1016/j.fss.2008.12.018
  3. S. B. Roh, S. K. Oh, and W. Pedrycz, "A Fuzzy Ensemble of Parallel Polynomial Neural Networks with Information Granules formed by Fuzzy Clustering," Knowledge-Based Systems, Vol. 23, No. 3, pp. 202-219, 2010. https://doi.org/10.1016/j.knosys.2009.12.002
  4. S. K. Oh, W. D. Kim, W. Pedrycz, and B. J. Park, "Polynomial-based Radial Basis Function Neural Networks (P-RBF NNs) Realized with the Aid of Particle Swarm Optimization," Fuzzy Sets and Systems, Vol. 163, No. 1, pp. 54-77, 2011. https://doi.org/10.1016/j.fss.2010.08.007
  5. S. K. Oh, W. Pedrycz, and S. B. Roh, "Genetically Optimized Hybrid Fuzzy Set-based Polynomial Neural Networks," Journal of the Franklin Institute, Vol. 348, No. 2, pp. 415-425, 2011. https://doi.org/10.1016/j.jfranklin.2010.11.005
  6. S. B. Roh, S. C. Joo, W. Pedrycz, and S. K. Oh, "The development of fuzzy radial basis function neural networks based on the concept of information ambiguity," Neurocomputing, Vol. 73, No.13-15, pp. 2464-2477. 2010. https://doi.org/10.1016/j.neucom.2010.05.006
  7. James C. Bezdek, Robert Ehrlich, William Full "FCM: The Fuzzy C-Means Clustering Algorithm" Computers & Geosciences, Vol. 10, No. 2-3, pp. 191-203, 1984. https://doi.org/10.1016/0098-3004(84)90020-7
  8. S. P. Lloyd, "Least Squares Quantization in PCM," IEEE Trans. on Inf. Theory, Vol. 28, No. 2, pp. 129-137, 1982. https://doi.org/10.1109/TIT.1982.1056489
  9. J. Wang, J. Liu, and L. Liu, "A Mountain Means Clustering Algorithm," Intelligent Control and Automation, (WCICA 2008. 7th World Congress on), pp. 5045-5049, 2008.
  10. N. R. Pal, and D. Chakraborty, "Mountain and Subtractive Clustering Method: Improvements and Generalizations," International Journal of Intelligent Systems, Vol. 15, No. 4, pp. 329-341, 2000. https://doi.org/10.1002/(SICI)1098-111X(200004)15:4<329::AID-INT5>3.0.CO;2-9
  11. J. Holland, "Adaptation In Natural and Artificial Systems," University of Michigan Press, 1975.
  12. J. Kennedy and R. Eberhart, "Particle Swarm Optimization," Proc. of IEEE International Conference on Neural Networks, Vol. 4, pp. 1942-1948, 1995.
  13. M. Dorigo and L.M. Gambardella, "Ant Colony System : A Cooperative Learning Approach to the Traveling Salesman Problem," IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp. 53-66, 1997. https://doi.org/10.1109/4235.585892
  14. D. Karaboga, B. Akay, "A Survey: Algorithms Simulating Bee Swarm Intelligence," Artificial Intelligence Review, Vol. 31, No. 1, pp. 68-85, 2009.

Acknowledgement

Grant : U-city 보안감시 기술협력센터

Supported by : 한국연구재단, 경기도