Neural network based identification of hysteresis in human meridian systems

Tan, Y. ; Dong, R. ; Chen, H. ; He, H.

International Journal of Applied Mathematics and Computer Science, Tome 22 (2012) no. 3, pp. 685-694.

Résumé

Developing a model based digital human meridian system is one of the interesting ways of understanding and improving acupuncture treatment, safety analysis for acupuncture operation, doctor training, or treatment scheme evaluation. In accomplishing this task, how to construct a proper model to describe the behavior of human meridian systems is one of the very important issues. From experiments, it has been found that the hysteresis phenomenon occurs in the relations between stimulation input and the corresponding response of meridian systems. Therefore, the modeling of hysteresis in a human meridian system is an unavoidable task for the construction of model based digital human meridian systems. As hysteresis is a nonsmooth, nonlinear and dynamic system with a multi-valued mapping, the conventional identification method is difficult to be employed to model its behavior directly. In this paper, a neural network based identification method of hysteresis occurring in human meridian systems is presented. In this modeling scheme, an expanded input space is constructed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. For this purpose, a modified hysteretic operator is proposed to handle the extremum-missing problem. Then, based on the constructed expanded input space with the modified hysteretic operator, the so-called Extreme Learning Machine (ELM) neural network is utilized to model hysteresis inherent in human meridian systems. As hysteresis in meridian system is a dynamic system, a dynamic ELM neural network is developed. In the proposed dynamic ELMneural network, the output state of each hidden neuron is fed back to its own input to describe the dynamic behavior of hysteresis. The training of the recurrent ELM neural network is based on the least-squares algorithm with QR decomposition.

Détail

Keywords: acupuncture point, hysteresis, identification, neural network, human meridian, traditional Chinese medicine
Mots-clés : punkt akupunktury, histereza, sieć neuronowa, tradycyjna medycyna chińska

Bibliographie

[1] Ahn, C., Colbert, A. P., Anderson, B. J., Martinsen, O. G., Hammerschlag, R., Cina, S., Wayne, P.M and Langevin, H. M. (2008). Electrical properties of acupuncture points and meridians: A systematic review, Bioelectromagnetics 29(4): 245-256.

[2] Dong, R. and Tan, Y. (2009). Modeling hysteresis in piezoceramic actuators using modified Prandtl-Ishlinskii model, Physica B 404(8-11): 1336-1342.

[3] Ge, P. and Jouaneh, M. (1995). Modeling hysteresis in piezoceramic actuators, Precision Engineering 17(3): 211-221.

[4] Huang, G. Zhu, Q. and Siew, C. (2006). Extreme learning machine: Theory and application, Neurocomputing 70: 489-501.

[5] Huang, G. and Chen, L. (2007). Convex incremental extreme learning machine, Neurocomputing 70(16-18): 3056-3062.

[6] Hu, H. and Mrad, R. (2003). On the classical Preisach model for hysteresis in piezoceramic actuators, Mechatronics 13(2): 85-94.

[7] Macki, J. W., Nistri and Zecca, P. (1993). Mathematical models for hysteresis, SIMAC Review 35(1): 94-123.

[8] Trentini, F. J., Thompson, B. and Erlichman, J. S. (2005). The antinociceptive effect of acupressure in rats, The American Journal of Chinese Medicine 33(1): 143-150.

[9] Tsuei, J. J. (1998). A modern interpretation of acupuncture and the meridian system, 2nd International Conference on Bioelectromagnetism, Melbourn, Australia, pp. 177-182.

[10] Wang, Z., Tan, Y. and Su, M. (2009). Modeling of meridian channels, Proceedings of the International Conference on Biomedical Electronics and Devices, Porto, Portugal, pp. 167-172.

[11] Yang, H. (1997). The research and application of the dynamic testing system for point skin resistance, Journal of Biomedical Engineering 16(1): 41-50.

[12] Yamamoto, Y. and Yamamoto, T. (1979). Dynamic system for the measurement of electrical skin impedance, Medical and Biological Engineering and Computing 17(1): 135-137.

[13] Zhao, X. and Tan, Y. (2006). Neural network based identification of Preisach-type hysteresis in piezoelectric actuator using hysteretic operator, Sensors and Actuators A 126(2): 306-311.

[14] Zhao, X. and Tan, Y. (2008). Modeling hysteresis and its inverse model using neural networks based on expanded input space method, IEEE Transactions on Control Systems Technology 16(3): 484-490.

[15] Zhang,W., Xu, R. and Zhu, Z. (1999). The influence of acupuncture on the impedance measured by four electrodes on meridians, Acupuncture Electro-Therapeutics Research 24(3-4): 181-188.