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Minimal Structure of Self-Organizing HCMAC Neural Network Classifier

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Abstract

The authors previously proposed a self-organizing Hierarchical Cerebellar Model Articulation Controller (HCMAC) neural network containing a hierarchical GCMAC neural network and a self-organizing input space module to solve high-dimensional pattern classification problems. This novel neural network exhibits fast learning, a low memory requirement, automatic memory parameter determination and highly accurate high-dimensional pattern classification. However, the original architecture needs to be hierarchically expanded using a full binary tree topology to solve pattern classification problems according to the dimension of the input vectors. This approach creates many redundant GCMAC nodes when the dimension of the input vectors in the pattern classification problem does not exactly match that in the self-organizing HCMAC neural network. These redundant GCMAC nodes waste memory units and degrade the learning performance of a self-organizing HCMAC neural network. Therefore, this study presents a minimal structure of self-organizing HCMAC (MHCMAC) neural network with the same dimension of input vectors as the pattern classification problem. Additionally, this study compares the learning performance of this novel learning structure with those of the BP neural network,support vector machine (SVM), and original self-organizing HCMAC neural network in terms of ten benchmark pattern classification data sets from the UCI machine learning repository. In particular, the experimental results reveal that the self-organizing MHCMAC neural network handles high-dimensional pattern classification problems better than the BP, SVM or the original self-organizing HCMAC neural network. Moreover, the proposed self-organizing MHCMAC neural network significantly reduces the memory requirement of the original self-organizing HCMAC neural network, and has a high training speed and higher pattern classification accuracy than the original self-organizing HCMAC neural network in most testing benchmark data sets. The experimental results also show that the MHCMAC neural network learns continuous function well and is suitable for Web page classification.

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References

  1. Albus J.S. (1975) A new approach to manipulator control: The cerebellar model articulation controller (CMAC). J. Dyn. Syst. Meas. Control Trans. ASME, 220–227

  2. Albus J.S. Data storage in the cerebellar model articulation controller (CMAC), J. Dyn. Sys. Meas. Control. Trans. ASME, (1975), 228–233.

  3. Hu J., Pratt J., Pratt G. Stable adaptive control of a bipedal walking; Robot with CMAC neural networks. Proceedings of 1999 IEEE International Conference on Robotics and Automation, Detroit, MI, USA, vol. 2, pp. 1050–1056, 1999.

  4. W.T. Miller F.H. Glanz (1990) ArticleTitleCMAC: An associative Neural Network alternative to backpropagation Proceedings of the IEEE 78 IssueID10 1561–1567 Occurrence Handle10.1109/5.58338

    Article  Google Scholar 

  5. Glanz F.H., Miller W.T., Kraft L.G. An Overview of the CMAC neural network, Proceedings of 1991 IEEE Neural Networks for Ocean Engineering, Washington, DC, USA, 1991, pp. 301–308.

  6. C.M. Chen (2004) ArticleTitleIncremental personalized web page mining utilizing self-organizing HCMAC neural network Web Intelligence Agent Syst. Int. J. 2 21–38

    Google Scholar 

  7. N.E. Cotter T.J. Guillerm (1991) ArticleTitleThe CMAC and a theorem of Kolmogorov Neural Networks 5 221–228

    Google Scholar 

  8. Zhang K., Qian F. Fuzzy CMAC and its application, Proceedings of the 3th World Congress on Intelligent Control and Automation, Hefei, China, pp. 944–947, 2000.

  9. Sayil S., Lee K.Y. A hybrid maximum error algorithm with neighborhood training for CMAC, In Proceedings of IJCNN’02, vol. 1, pp. 165–170, 2002.

  10. Weruaga L. Active Training on the CMAC Neural Network, In Proceedings of IJCNN’2004, vol. 2, pp. 855–860, 2004.

  11. Hu, J. S. and Hu, G. W.: FCMAC based on extreme element algorithm, In Proceedings of The Third International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1889–1894, 2004.

  12. L. Zhang Q. Cao J. Lee Y. Zhao (2004) ArticleTitleA modified CMAC algorithm based on credit assignment Neural Process. Lett. 20 1–10 Occurrence Handle10.1023/B:NEPL.0000039430.04088.78

    Article  Google Scholar 

  13. S.H. Lane D.A. Handelman J.J. Gelfand (1992) ArticleTitleTheory and development of higher-order CMAC neural networks IEEE Control Syst. Mag. 12 IssueID2 23–30 Occurrence Handle10.1109/37.126849

    Article  Google Scholar 

  14. C.S. Lin C.K. Li (2000) ArticleTitleA sum-of-product neural network (SOPNN) Neurocomputing 30 273–291 Occurrence Handle10.1016/S0925-2312(99)00130-7

    Article  Google Scholar 

  15. Lin C.S., Li C.K. A low-dimensional-CMAC-based neural network, Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Beijing, China, vol. 2, pp. 1297–1302, 1996.

  16. Li B., Elliman D. Fuzzy Classification by a CMAC network, Proceedings of IEEE International Conference on Tools with Artificial Intelligence, Newport Beach, CA, USA, 1997, pp. 392–395.

  17. P.E.M. Almeida M.G. Simoes (2003) ArticleTitleParametric CMAC networks: Fundamentals and applications of a fast convergence neural structure IEEE Trans. Industry Appl. 39 IssueID5 1551–1557

    Google Scholar 

  18. K.S. Hwang Y.P. Hsu (2003) ArticleTitleA new approach to improving a CMAC architecture IEEE International Conference on Neural Network and Signal Processing 1 320–323

    Google Scholar 

  19. Lin C.J., Chen H.J., Lee C.Y. A self-organizing recurrent fuzzy CMAC model for dynamic system identification, In Proceedings of IEEE International Conference on Fuzzy Systems, vol. 2, pp. 697–702, 2004

  20. Horváth G. Kernel CMAC with improved capability, In proceedings of IJCNN’2004, vol. 1, pp. 681–686, 2004.

  21. C.S. Lin H.: Kim (1995) ArticleTitleSelection of learning parameters for CMAC-based adaptive critic learning IEEE Trans. Neural Networks 6 642–647 Occurrence Handle10.1109/72.377969

    Article  Google Scholar 

  22. Hsu Y.P., Hwang K.S., Pao C.Y., Wang J.S. A new CMAC neural network architecture and its ASIC realization, In Proceedings of the ASP-DAC 2000, Asia and South Pasific Design Automation Conference, pp. 481–484, 2000.

  23. T.W. Miller F.H. Glanz L.G. Kraft (1987) ArticleTitleApplication of a general learning algorithm to the control of Robotic manipulators Int. J. Robotic Res. 6 IssueID2 84–98

    Google Scholar 

  24. R.O. Shelton (1992) ArticleTitlePeterson J.K. Controlling a truck with an adaptive critic CMAC design Simulation 58 IssueID5 319–326

    Google Scholar 

  25. Miller W.T., Aldrich Rapid learning using CMAC neural networks: Real time control of an unstable system, Proceedings of the 5th IEEE International Symposium on Intelligent Control 1990, Philadelphia, PA, pp. 465–470, 1990.

  26. Y.F. Wong A. Sideris (1992) ArticleTitleLearning convergence in the cerebellar model articulation controller IEEE Trans. Neural Networks 3 IssueID1 115–121 Occurrence Handle10.1109/72.105424

    Article  Google Scholar 

  27. C.S. Lin C.T. Chiang (1997) ArticleTitleLearning convergence of CMAC technique IEEE Trans. Neural Networks 8 IssueID6 1281–292

    Google Scholar 

  28. Hu J., Pratt F. Self-organizing CMAC neural networks and adaptive dynamic control, Proceedings of the 1999 IEEE International Symposium on Intelligent Control/Intelligent Systems and Semiotics, Cambridge, MA, USA, pp. 259–265, 1999.

  29. Menozzi, A. and Chow, M.-Y.: On the training of a multi-resolution CMAC neural network, Proceedings of the IEEE International Symposium on Industrial Electronics, ISIE’97, Guimaraes, Portugal, vol. 3, pp. 1201–1205, 1997.

  30. Zhong, L., Zhongming, Z. and Chongguang, Z.: The unfavourable effects of hashing coding on CMAC convergence and compensatory measure, IEEE International Conference on Intelligent Processing Systems, Beijing, China, October 1997, pp. 419–422.

  31. Wang, Z.-Q., Schiano, J. L. and Ginsberg, M.: Hash-coding in CMAC neural networks, IEEE International Conference on Neural Networks, Washington, DC, USA, vol. 3, pp. 1698–1703, 1996.

  32. C.S. Lin C.K. Li (1999) ArticleTitleA Memory-based self-generated basis function neural network Inter. J. Neural Syst. 9 IssueID1 41–59

    Google Scholar 

  33. UCI Machine Learning Repository, web available from http://www.ics.uci.edu/ ~mlearn/MLRepository.html.

  34. J.C. Jan S.L. Hung (2001) ArticleTitleHigh-order MS_CMAC neural network IEEE Trans. Neural Networks 12 IssueID3 598–603 Occurrence Handle10.1109/72.925562

    Article  Google Scholar 

  35. J. Moody (1989) ArticleTitleFast-learning in Multi-resolution Hierarchies Adv. Neural Infor. Processing Syst. 14 IssueID1 29–38 Occurrence Handle90b:18014

    MathSciNet  Google Scholar 

  36. Kim H., Lin C.S. Use of adaptive resolution for better CMAC learning, Proceedings of International Joint Conference on Neural Networks, Baltimore, MD, USA, vol. 1, pp. 517–522, 1992.

  37. C.S. Berger (1994) ArticleTitleLinear splines with adaptive mesh sizes for modelling nonlinear dynamic systems IEEE Proceedings of Control Theory Application 141 IssueID5 277–286 Occurrence Handle0925.93330

    MATH  Google Scholar 

  38. C.T. Chiang C.S. Lin (1996) ArticleTitleCMAC with general basis functions Neural Networks 9 IssueID7 1199–1211

    Google Scholar 

  39. H.M. Lee C.M. Chen Y.F. Lu (2003) ArticleTitleA self-organizing HCMAC neural network classifier IEEE Trans. Neural Networks 14 IssueID1 1–13

    Google Scholar 

  40. C.E. Shannon (1948) ArticleTitleA mathematical theory of communication Bell Syst. Tech. J. 27 379–423 Occurrence Handle10,133e

    MathSciNet  Google Scholar 

  41. Bazaraa, M. S., Sherali, H. D. and Shetty, C. M.: Nonlinear Programming Theory and Algorithms, 2nd edition, John Wiley & Sons, Inc., pp. 270–271, 1993.

  42. D.E. Rumelhart G.E. Hinton R.J. Williams (1986) ArticleTitleLearning internal representation by error propagation Parallel Distribut. Process. 1 318–362

    Google Scholar 

  43. LIBSVM, web available from http://www.csie.ntu.edu.tw/~cjlin/libsvm/

  44. M. Pazzani D. Billsus (1997) ArticleTitleLearning and revising user profiles: The identification of interesting web sites Machine Learning 27 313–331 Occurrence Handle10.1023/A:1007369909943

    Article  Google Scholar 

  45. Pazzani, M., Muramatsu, J. and Billsus, D.: Syskill & Webert: Identifying interesting web sites, Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 54–59, 1996.

  46. Salton G. Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer, Addison-Wesley, 1989.

  47. Francis W., Kucera H. Frequency Analysis of English Usage, New York, 1982.

  48. Lee H.M., Chen C.M., Tan C.C. An intelligent web-page classifier with fair feature-subset selection, Proceedings of Joint 9th IFSA World Congress and 20th NAFIP International Conference (IFSA/NAFIPS’01), Vancouver, Canada, vol. 1, pp. 395–400, 2001.

  49. Lewis, D. D. and Gale, W. A.: A sequential algorithm for training text classifiers, Proceedings of SIGIR 1994, pp. 3–12.

  50. Lewis D.D., Schapire R.E., Callan J.P., Papka R. (1996) Training algorithms for linear text classifier, In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’96), 1996, pp. 298–306.

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Authors and Affiliations

  1. Graduate Institute of Learning Technology, National Hualien University of Education, Hualien, Taiwan

    Chih-Ming Chen

  2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Yung-Feng Lu

  3. Institute of Applied Electronic Technology, National Taiwan Normal University, Taipei, Taiwan

    Chin-Ming Hong

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  1. Chih-Ming Chen
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  2. Yung-Feng Lu
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Correspondence to Chih-Ming Chen.

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Chen, CM., Lu, YF. & Hong, CM. Minimal Structure of Self-Organizing HCMAC Neural Network Classifier. Neural Process Lett 23, 201–228 (2006). https://doi.org/10.1007/s11063-006-6277-0

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