Neural network based identification of hysteresis in human meridian systems

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.

QR factorization based Incremental Extreme Learning Machine with growth of hidden nodes

“…δh k+1 = q k+1 · r k+1,k+1 = h k+1 − (q 1 · r 1,k+1 + · · · + q k · r k,k+1 ) = h k+1 − Q k δr k+1 (18) r k+1,k+1 = δh T k+1 δh k+1 (19) …”

“…To further reduce computational complexity, hence the training time, we propose another incremental algorithm based on QR factorization, as described in Section 4. Although QR factorization is already used in ELM in some literature [6,12,19], this technique is not applied to hidden node incremental ELM yet [9]. Comparison simulations among ELM, EM-ELM and the proposed one with several datasets are demonstrated in Section 5.…”

Structural Identification of Nonlinear Dynamic Systems

“…In [18] modification of self-adjusted differential evolutionary algorithm for an estimation of parameters the hysteresis, described BW model, is offered. Approaches to parametric identification of the hysteresis, described by various nonlinear functions, are considered in [19][20][21]. From review of publications follows that the overwhelming number of works is devoted problems of parametric identification of nonlinear systems subject to different various (and sometimes and full) level of the a priori information.…”

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

“…Moreover, special types of neural networks are used for adaptive synthesis of the wavelet transform [8], image segmentations [9] and systems identification and diagnosis [10,11], which proves that ANNs can be used in many technical areas. Based on this fact, for the prediction of corrections, the method based on artificial neural networks is examined in detail in this paper.…”