# Neural network based identification of hysteresis in human meridian systems

Yonghong Tan; Ruili Dong; Hui Chen; Hong He

International Journal of Applied Mathematics and Computer Science (2012)

- Volume: 22, Issue: 3, page 685-694
- ISSN: 1641-876X

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topYonghong Tan, et al. "Neural network based identification of hysteresis in human meridian systems." International Journal of Applied Mathematics and Computer Science 22.3 (2012): 685-694. <http://eudml.org/doc/244062>.

@article{YonghongTan2012,

abstract = {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 ELM neural 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.},

author = {Yonghong Tan, Ruili Dong, Hui Chen, Hong He},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {acupuncture point; hysteresis; identification; neural networks; human meridian; traditional Chinese medicine},

language = {eng},

number = {3},

pages = {685-694},

title = {Neural network based identification of hysteresis in human meridian systems},

url = {http://eudml.org/doc/244062},

volume = {22},

year = {2012},

}

TY - JOUR

AU - Yonghong Tan

AU - Ruili Dong

AU - Hui Chen

AU - Hong He

TI - Neural network based identification of hysteresis in human meridian systems

JO - International Journal of Applied Mathematics and Computer Science

PY - 2012

VL - 22

IS - 3

SP - 685

EP - 694

AB - 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 ELM neural 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.

LA - eng

KW - acupuncture point; hysteresis; identification; neural networks; human meridian; traditional Chinese medicine

UR - http://eudml.org/doc/244062

ER -

## References

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