人工神经网络对VOCs的自动识别

利用人工神经网络(ANN)对严重混叠的傅里叶变换红外光谱图进行了定性和定量解析。通过大量模拟数据训练神经网络后,引用了新的评价标准——逼近度来选择最优网络模型。利用此优化网络对两类光谱图进行了解析,考察了网络的泛化能力。结果表明：该网络不仅能够对两组分同时存在时的样本进行准确解析,而且对于未知单组分光谱图,也能够进行准确鉴别和定量分析。可见, 该研究为人工神经网络在单组分和多组分未知物的定性和定量分析方面提供了一种新思路。     

区域经济系统的ARX参数模型估计

利用系统辨识的方法,对非线性系统进行输入、输出分析,利用模糊评判的输入、输出数据,从多个非线性模型中,确定出一个能够反映区域经济系统特征的模型,从而形成了一种建构在模糊评判基础上系统辨识新思路.     

Development of data-based model-free representation of non-conservative dissipative systems

A general procedure is presented for developing data-based, non-parametric models of non-linear multi-degree-of-freedom, non-conservative, dissipative systems. Two broad classes of methods are discussed: one relying on the representation of the system restoring forces in a polynomial-basis format, and the other using artificial neural networks to map the complex transformations relating the system state variables to the needed system outputs. A non-linear two-degree-of-freedom system is used to formulate the approach under discussion and to generate synthetic data for calibrating the efficiency of the two methods in capturing complex non-linear phenomena (such as dry friction, hysteresis, dead-space non-linearities, and polynomial-type non-linearities) that are widely encountered in the applied mechanics field. Subsequently, a reconfigurable test apparatus was used to generate experimental measurements from a physical non-linear “joint” involving two-dimensional motion (translation and rotation) and complicated interaction forces between the different motion axes, among its internal elements. Both the polynomial-basis approach and the neural network method were used to develop high-fidelity, non-parametric models of the physical test article. The ability of the identified models to accurately “generalize” the essential features of the non-linear system was verified by comparing the predictions of the models with experimental measurements from data sets corresponding to different excitations than those used for identification purposes. It is shown that the identification techniques under discussion can be useful tools for developing accurate simulation models of complex multi-dimensional non-linear systems under broadband excitation.     

Reconstruction families of possibilistic structure systems

A method is presented for characterizing the family of overall systems reconstructable from a given possibilistic structure system. The technique is elaborated in terms of fuzzy relation equations.It is demonstrated that the reconstruction family of a given structure system is equivalent to the set of solutions of a special type of fuzzy relation equation. The solution set is partially ordered, and contains both minimal solutions and a unique maximum solution. When these elements are identified, all members of the reconstruction family are determined.Another characteristic of the reconstruction family is its reconstruction uncertainty, a measure of which is also developed in this paper. This measure is used to define an identifiability quotient that expresses the degree of confidence with which we may identify a single overall system given a particular structure system.     

Estimation of plate elastic moduli through vibration testing

This paper considers the identification problem for 2D-structures by comparing a modal method with a new method based on the estimation of the dispersion equation in k-space. Both methods are validated by numerical simulation and by measurements based on an acoustic holography experiment.     

主动噪声控制声学通道的实验辨识

传统的自适应滤波器不能直接用于主动噪声控制，因为扬声器和误差传声器之间的声学通道可能使系统不稳定。本文根据声学通道的相应特性，建立补偿滤波器，提出了用实验方法辨识声学通道传输特性。在房间实验中研究了单通道相位因子的变化特性，并进一步用于飞机人多通道主动噪声控制，给出了相应的实验结果。     

激光光解NO2产物NO(X2∏)的REMPI离子谱的标识与研究

利用Nd:YAG激光器三倍频、输出波长为＝ 355 nm的激光光解NO2分子,可产生NO分子,将NO分子通过共振增强多光子电离(REMPI resonance enhanced multiphoto ionization)及飞行时间(TOF time of flight)质谱技术,获得振转态分辨的NO( X2∏,υ″J″)离子谱NOγ(0,0),γ(0,1),γ(1,1).通过理论计算 , 可将NO离子带的P、R、Q支线进行标识.NO分子的离子信号强度与UV电离激光能量(λ≈226 nm)之间关系能用二次方曲线很好拟合.它表明NO分子是通过(1+1)双光子吸收而电离.这些结果对NO分子的电离动力学提供了有益的信息.     

Identification of switched linear systems using subspace and integer programming techniques

We consider the identification of a switched linear system which consists of linear sub-models, with a rule that orchestrates the switching mechanism between the sub-models. Taking a set of switched linear systems and using a state space framework, we show that it is possible to combine subspace methods with mixed integer programming for system identification. The states of the system are first extracted from input–output data using sub-space methods. Once the state variables are known, the switched system is re-written as a mixed logical dynamical (MLD) system and the model parameters are calculated for via mixed integer programming. We report an example at the end of this paper together with simulation results in the presence of noise.