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Badania nad Radix Sophorae Subprostratae za pomocą techniki nieliniowych fingerprintów chemicznych (pełne spektrum)

Ding F., She Y., Fang X., Li S., Liang Y., Zhang T., Si S.

Przemysł Chemiczny

2018

T. 97, nr 11

1884--1888

Technika nieliniowego chemicznego fingerprintu NCF (non-linear chemical fingerprint) może być z powodzeniem stosowana do kompleksowej oceny układów złożonych. NCF wykorzystano do badania korzenia rośliny Sophorae Subprostratae ( Sophora subprostrata Chun et T. Chen), stosowanego w tradycyjnej medycynie chińskiej. W badaniach uwzględniono parametr jakościowy i parametr ilościowy reakcji (czas i potencjał) oraz ustalono matematyczną zależność pomiędzy nimi. W układzie oscylacyjnym złożonym z bromianu sodu, kwasu siarkowego, acetonu i siarczanu manganu optymalny zakres detekcji składników Radix Sophorae Subprostratae uzyskano dla ilości 0,7-1,5 g sproszkowanego korzenia. Wykazano pewne różnice w chemicznym składzie próbek Radix Sophorae Subprostratae pozyskanych z różnych regionów. Uzyskane wyniki porównano z wynikami analizy chromatograficznej.

Radix Sophorae Subprostratae (traditional Chinese medicine) was evaluated in a fixed system consisting of NaBrO3, H2SO4, Me2CO and MnSO4 do det. its fingerprint. Three oscillation areas were established. The results agreed with chromatog. detd. data.

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

2012

Vol. 22, no. 3

685-694

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.