基于聚类模糊神经网络的非线性电路故障诊断

提出了一种基于聚类算法和模糊神经网络的非线性模拟电路故障诊断方法。通过一个无监督的聚类算法自组织地确定模糊规则的数目并生成一个初始的故障诊断模糊规则库，构造了一类模糊神经网络，通过训练调整网络权值，使故障诊断模糊规则库的分类更加精确，并通过仿真实验验证了该方法的有效性。     

基于优化BP神经网络的PID控制研究与仿真

PID控制要取得较好的控制效果．就必须通过调整好比例、积分和微分三种控制作用，形成控制量中既相互配合又相互制约的关系。优化BP神经网络是一种前向神经元网络，具有学习速率快、振荡小、精度高的优点，将其隐含层单元分别作为比例（P）、积分（I）、微分（D）单元。可以建立参数自学习的PID控制器。仿真结果表明基于优化BP神经网络的PID控制器具有较好的自学习和自适应性。     

Backscattering wave mode analysis of an elastic tube and parameter reconstruction

I.IntroductionInthestudyofundcrwatcrtargctidcntification,moreandmoreattentionhasbcenpaidtotheinversescatteringprob1cm.Alargekindofobjcctscanbesimplificdintocylinders,spheresore11ipsoidswhilcstudyingtheirscattcringficIds.Thedirectprob1cmhasbeendeve1opedwel1andpcop1caredevotingtothcstudyofthcinvcrscprob1emfortargctidentification.Thedirectprob1emofthescatteringfrome1asticcylindcrsortubcsandtheinverseproblcmofthescat-teringfromsofttubcshavebccninvcstigatcd.['-']Inthispapcr,thedircctandinvcrsepro…     

低杂波电流驱动的密度极限分析

用低杂波的可的性条件，参量不稳定条件和功率耦合谱分析了低杂波电流驱动。由于波的可近性条件限制耦合谱中平行相速度较大的波进入等离子体中心，参量不稳定性使平行相速度较低的波与离子相互作用，随着等离子体密度的增中，这些作用越来越明显，最终导致低杂波不能在中心与电子相互作用，驱动电流消失，这就是所谓密度极限。在本文建立的模型基础上的计算结果与实验结果符合较好。     

A class of loss functions of catenary form

The catenary form of loss function is considered in the framework of Bayesian decision theory. The mathematical tractability of this form seems to be unrecognized; it contains quadratic loss as a limiting case. For various probability distributions expressions are given for posterior analysis, and limiting properties are investigated.     

高性能主轴变频器的设计和实现

本文详细介绍了一种基于DSP的全数字主轴电机变频器的设计方案，并且给出了～系列的优化策略以提高系统性能。为了保证主轴电机响应的快速性和运转的平稳性，系统采用了间接矢量控制策略，考虑到该控制策略对于电机参数有一定的依赖性，因此采用离线辨识的方法来获取控制所需的电机参孰为了便于全数字化系统的实现，以及提高母线电压的利用率，系统采用了改进的空间矢量脉宽调制策略；为了提高主轴电机在弱磁区的带载能力，以及延长主轴电机恒功率运转的区间，系统采用了电压最大化利用的优化弱磁控制策略。最后，给出了依据本方案设计的主轴变频器的试验结果。结果证明了本方案是可行的，达到了预期目标。     

A novel approach to parameter extraction for on-chip transformers

In this paper, a novel approach to parameter extraction for on-chip transformers based on four-port Y-parameter is presented. Compared to the previous researches, the proposed fully parameter extraction from Y-parameter has better accuracy and feasibility for both the simulation before prototype and the improvement of the design after the measurement. And the 4-port model of transformer provides the solution of the fully differential applications. The model agrees very well with measurements for a variety of transformer configuration up to its self-resonant frequency.     

On stochastic parameter estimation using data assimilation

Data assimilation-based parameter estimation can be used to deterministically tune forecast models. This work demonstrates that it can also be used to provide parameter distributions for use by stochastic parameterization schemes. While parameter estimation is (theoretically) straightforward to perform, it is not clear how one should physically interpret the parameter values obtained. Structural model inadequacy implies that one should not search for a deterministic “best” set of parameter values, but rather allow the parameter values to change as a function of state; different parameter values will be needed to compensate for the state-dependent variations of realistic model inadequacy. Over time, a distribution of parameter values will be generated and this distribution can be sampled during forecasts. The current work addresses the ability of ensemble-based parameter estimation techniques utilizing a deterministic model to estimate the moments of stochastic parameters. It is shown that when the system of interest is stochastic the expected variability of a stochastic parameter is biased when a deterministic model is employed for parameter estimation. However, this bias is ameliorated through application of the Central Limit Theorem, and good estimates of both the first and second moments of the stochastic parameter can be obtained. It is also shown that the biased variability information can be utilized to construct a hybrid stochastic/deterministic integration scheme that is able to accurately approximate the evolution of the true stochastic system.     

Adaptive synchronization and lag synchronization of uncertain dynamical system with time delay based on parameter identification

In this paper, the adaptive synchronization and lag synchronization are considered for uncertain dynamical system with time delay based on parameter identification and a novel control method is then further given using the Lyapunov functional method. With this new and effective method, parameter identification and lag synchronization can be achieved simultaneously. Simulation results are given to justify the theoretical analysis in this paper.