Select Subject

All (8497)

Agronomy (157)

Animal Husbandry,... (64)

Aquatic Science (16)

Astronomy (36)

Aviation (21)

Basic Medicine (182)

Biology (498)

Chemical Engineering (102)

Chemistry (631)

Civil and Archite... (105)

Clinical Medicine (230)

Computer Science ... (847)

Dynamic and Elect... (120)

Earth Science (118)

Economics (65)

Education (30)

Electrics, Commun... (900)

Energy Science an... (66)

Engineering and T... (14)

Environmental Sci... (140)

Food Science and ... (63)

Forestry (36)

Hydraulic Enginee... (40)

Mechanics (240)

Metallurgical Eng... (32)

Mine Engineering ... (38)

Nuclear Science a... (21)

Pharmacy (98)

Physics (914)

Preventive Medici... (36)

Psychology (20)

Security Science ... (17)

Sports Science (6)

Surveying and Map... (15)

Textile Science a... (16)

Traditional Chine... (55)

Transportation En... (52)

Select/Unselect all

For Selected Papers

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list


	1. Structure Identification for Gene Regulatory Networks via Linearization and Robust State Estimation
	Xiong Jie, Zhou Tong
	Information Science and System Science 21 February 2014
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:Inferring causal relationships among numerous cellular components is one of the fundamental problems in understanding biological behaviors. The model of gene regulatory networks (GRNs) is widely considered as a nonlinear dynamic stochastic model consisting of a gene measurement equation and a gene regulation equation, in which the extended Kalman filter (EKF) is sometimes used for estimating both the model parameters and the actual value of gene expression levels. First-order approximations, however, unavoidably result in modelling errors, but the EKF based method does not take either unmodelled dynamics or parametric uncertainties into account, which makes its estimation performances not very satisfactory. As a result, estimation performances of the EKF based method may not be satisfied with slow convergence speed and low estimation accuracy. To overcome these problems, a sensitivity penalization based robust state estimator is suggested for reconstructing the structure of a GRN. The suggested method has been used to identify an artificially constructed non-linear GRN. Compared with the widely adopted EKF based method, simulation results show that the convergence speed is distinctly improved, and parametric estimation accuracy is significantly increased, which make both the false positive error and the false negative error significantly reduced. Moreover, computation results with a real GRN of extit{yeast} show that the proposed method can identify causal relationships effectively, which may help us to better understand the structure and dynamics of GRNs in practice.
	TO cite this article:Xiong Jie, Zhou Tong. Structure Identification for Gene Regulatory Networks via Linearization and Robust State Estimation[OL].[21 February 2014] http://en.paper.edu.cn/en_releasepaper/content/4586446


	2. A Kalman-Filter-Based Approach to Identification of SARX Systems
	Xiong Jie,Zhou Tong
	Information Science and System Science 27 March 2012
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:Identification of switching autoregressive exogenous (SARX) systemsis discussed in this paper. On the basis of a relation between theKalman filter and recursive least squares (RLS) estimation, it isshown that when external noises are white and Gaussian, then,some stochastic processes can be constructed whichare white if and only if experimental data are generated by the samesub-ARX model. Based on this observation, a method is developed toidentify switching times of a SARX system, and a procedure formerging experimental data generated by the same sub-model isobtained. Using these merged data, estimation of sub-models can becarried out by resorting to standard linear identificationtechniques. As a main feature, the obtained SARX systemidentification algorithm depends neither on the number of sub-models nor onthe order of each sub-model. Some numericalexperimental results are also included to illustrate theeffectiveness of the proposed algorithm.
	TO cite this article:Xiong Jie,Zhou Tong. A Kalman-Filter-Based Approach to Identification of SARX Systems[J].


	3. Frequency Domain MIMO Identification for Modeling of Structural Dynamics
	TANG WEI
	Information Science and System Science 14 July 2011
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:Frequency domain system identification is widely used in the area of structural dynamic analysis. However, due to modeling errors, disturbances and measurement noise, the identified model may be unstable. In this paper, we propose two algorithms to identify structural dynamics with prior knowledge of stable poles. The first algorithm is based on subspace identification, and the second algorithm is based on maximum likelihood in the frequency domain. The corn difference with previous methods in that the new algorithms consider the poles constraint. We show the benefits of the proposed approaches in a simple example where the results of the first algorithm are used as an initial estimate for the second algorithm.
	TO cite this article:TANG WEI. Frequency Domain MIMO Identification for Modeling of Structural Dynamics[OL].[14 July 2011] http://en.paper.edu.cn/en_releasepaper/content/4434863


	4. Robust System Identification of Continuous-Time Model from Frequency Response Function Data
	TANG Wei
	Information Science and System Science 19 April 2011
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:This paper addresses the numerical conditioning problem that arises in the continuous-time system identification case. To solve this problem, a new frequency domain weighted least squares estimator using matrix orthogonal polynomial basis (MOPB) is proposed, which allow us to model transfer function matrix without vector operation, and yield perfect condition number. The key idea is to expand the matrix fraction description model on MOPB. The construction of MOPB is described in this paper and the efficacy of this method is illustrated with a numerical example.
	TO cite this article:TANG Wei. Robust System Identification of Continuous-Time Model from Frequency Response Function Data[OL].[19 April 2011] http://en.paper.edu.cn/en_releasepaper/content/4423033


	5. BP Neural Network principle and MATLAB Simulation
	Xiong Xin,Nie Mingxin
	Information Science and System Science 19 May 2006
	Show/Hide Abstract \| Cite this paper︱Full-text:

Alert Name:
Alerting to:
Authentication email will be sent to your email address in 24 hours
Frequency:
Email Message Format:	Plain text Graphical(HTML)

Complete the form below and we will recommend the selected titles to your friends on your behalf. * Indicates a required field.
Your name*:
Your email address*:
Recipient's name*:
Recipient's email address*:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find the page(s) useful.

Your name:
Your email address:
Recipient's name:
Recipient's email address:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find this page useful.

Disclaimer: This message was sent to your friend using the "Send it to a friend" facility on the Sciencepaper Online’ WWW site, http://www.paper.edu.cn/en. The Sciencepaper Online is not responsible for the content of this email, and anything said in this email does not necessarily reflect the Sciencepaper Online's views.

	Check out RSS, or use RSS reader to subscribe this item