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Time-varying time-delay estimation for nonlinear systems using neural networks

100%
Tan Y.
International Journal of Applied Mathematics and Computer Science
|
2004
|
tom 14
|
nr 1
63-68
EN
Nonlinear dynamic processes with time-varying time delays can often be encountered in industry. Time-delay estimation for nonlinear dynamic systems with time-varying time delays is an important issue for system identification. In order to estimate the dynamics of a process, a dynamic neural network with an external recurrent structure is applied in the modeling procedure. In the case where a delay is time varying, a useful way is to develop on-line time-delay estimation mechanisms to track the time-delay variation. In this paper, two schemes called direct and indirect time-delay estimators are proposed. The indirect time-delay estimator considers the procedure of time-delay estimation as a nonlinear programming problem. On the other hand, the direct time-delay estimation scheme applies a neural network to construct a time-delay estimator to track the time-varying time-delay. Finally, a numerical example is considered for testing the proposed methods.
2
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Recursive identification algorithm for dynamic systems with output backlash and its convergence

51%
Dong R., Tan Q., Tan Y.
International Journal of Applied Mathematics and Computer Science
|
2009
|
tom 19
|
nr 4
631-638
EN
This paper proposes a recursive identification method for systems with output backlash that can be described by a pseudoWiener model. In this method, a novel description of the nonlinear part of the system, i.e., backlash, is developed. In this case, the nonlinear system is decomposed into a piecewise linearized model. Then, a modified recursive general identification algorithm (MRGIA) is employed to estimate the parameters of the proposed model. Furthermore, the convergence of the MRGIA for the pseudo-Wiener system with backlash is analysed. Finally, a numerical example is presented.
3
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Neural network based identification of hysteresis in human meridian systems

51%
Tan Y., Dong R., Chen H., He H.
International Journal of Applied Mathematics and Computer Science
|
2012
|
tom 22
|
nr 3
685-694
EN
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.
4
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Observability and controllability analysis for sandwich systems with backlash

51%
Luo N., Tan Y., Dong R.
International Journal of Applied Mathematics and Computer Science
|
2015
|
tom 25
|
nr 4
803-814
EN
In this paper, an approach to analyze the observability and controllability of sandwich systems with backlash is proposed. In this method, a non-smooth state-space function is used to describe the sandwich systems with backlash which are also non-smooth non-linear systems. Then, a linearization method based on non-smooth optimization is proposed to derive a linearized state-space function to approximate the non-smooth sandwich systems within a bounded region around the equilibrium point that we are interested in. Afterwards, both observability and controllability matrices are constructed and the methods to analyze the observability as well as controllability of sandwich system with backlash are derived. Finally, numerical examples are presented to validate the proposed method.
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