Threshold-type memristor-based crossbar array design and its application in handwritten digit recognition

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the Von Neumann architecture.Inspired by the real characteristics of physical memristive devices,we propose a threshold-type nonlinear voltage-controlled memristor mathematical model which is used to design a novel memristor-based crossbar array.The presented crossbar array can simulate the synaptic weight in real number field rather than only positive number field.Theoretical analysis and simulation results of a 2×2 image inversion operation validate the feasibility of the proposed crossbar array and the necessary training and inference functions.Finally,the presented crossbar array is used to construct the neural network and then applied in the handwritten digit recognition.The Mixed National Institute of Standards and Technology(MNIST)database is adopted to train this neural network and it achieves a satisfactory accuracy.

RELIABILITY ANALYSIS METHODFOR STRUCTURAL STIFFNESS

The reliability analysis for stiffness of large and complex structure systemsis present. and relative significance is introduced as a criterion to enumerate significantrandom variables. Simplications of limit state functions and selections of interpolatio

基于MOS管的忆阻器电路仿真器设计

运用互补金属氧化物半导体(Complementary Metal Oxide Semiconductor,CMOS)技术,设计了2款浮地型忆阻器电路仿真器,并研究电路仿真器在不同激励条件下的特性。理论推导表明,设计的电路仿真器的端口电压电流关系符合忆阻器与状态变量相关的欧姆定律方程。仿真结果表明,设计的忆阻器电路仿真器具有特有的捏滞回环特性,验证了其正确性和可行性。

Reliability Sensitivity Algorithm Based on Stratified Importance Sampling Method for Multiple Failure Modes Systems

Combining the advantages of the stratified sampling and the importance sampling,a stratified importance sampling metho(SISM) is presented to analyze the reliability sensitivity for structure with multiple failure modes.In the presented method,th variable space is divided into several disjoint subspace by n-dimensional coordinate planes at the mean point of the random vec tor,and the importance sampling functions in the subspaces are constructed by keeping the sampling center at the mean poin and augmenting the standard deviation by a factor of λ.The sample size generated from the importance sampling function i each subspace is determined by the contribution of the subspace to the reliability sensitivity,which can be estimated by iterativ simulation in the sampling process.The formulae of the reliability sensitivity estimation,the variance and the coefficient o variation are derived for the presented SISM.Comparing with the Monte Carlo method,the stratified sampling method and th importance sampling method,the presented SISM has wider applicability and higher calculation efficiency,which i demonstrated by numerical examples.Finally,the reliability sensitivity analysis of flap structure is illustrated that the SISM ca be applied to engineering structure.

Reliability and Sensitivity Analysis of Transonic Flutter Using Improved Line Sampling Technique

考虑飞机机翼结构参数的随机性,提供了一种有效的可靠性数字模拟方法—改进线抽样方法,来进行跨声速机翼颤振的概率和灵敏性分析。线抽样方法适用于高维、小失效概率和隐式极限状态方程的可靠性问题的概率和灵敏度分析,而不用引入任何隐式极限状态方程的近似替代函数。考虑机翼结构参数,如固有频率、重心位置参数和质量比等的不确定性,将改进线抽样技术应用于带有操纵面机构的二元机翼颤振问题的不确定性分析。采用基于计算流体动力学(CFD)技术的非定常气动力降阶方法,可得到基于结构模态坐标的非定常气动力模型,耦合结构状态方程和气动状态方程得到气动弹性系统的状态方程,通过求解状态方程中矩阵的特征值得到颤振的临界速度,并建立颤振的安全裕度方程。分别采用Monte Carlo模拟法和改进线抽样法进行随机不确定性分析,改进线抽样方法的计算效率比Monte Carlo模拟法有很大程度的提高。此外,灵敏度分析所得的灵敏度因子能够定量地反映随机参数对颤振临界速度的影响程度,从而为提高颤振临界速度的结构优化设计提供依据。

Importance measures for imprecise probability distributions and their sparse grid solutions

For the imprecise probability distribution of structural system, the variance based importance measures (IMs) of the inputs are investigated, and three IMs are defined on the conditions of random distribution parameters, interval distribution parameters and the mixture of those two types of distribution parameters. The defined IMs can reflect the influence of the inputs on the output of the structural system with imprecise distribution parameters, respectively. Due to the large computational cost of the variance based IMs, sparse grid method is employed in this work to compute the variance based IMs at each reference point of distribution parameters. For the three imprecise distribution parameter cases, the sparse grid method and the combination of sparse grid method with genetic algorithm are used to compute the defined IMs. Numerical and engineering examples are em-ployed to demonstrate the rationality of the defined IMs and the efficiency of the applied methods.

The CDF and its sensitivity analysis of stochastic structure with stochastic excitation by advanced stratified line sampling

For the stochastic structure with stochastic excitation, an advanced stratified line sampling (SLS) method is presented to obtain the cumulative distribution function (CDF) of the structural response and its sensitivity. The advanced stratified line sampling method introduces a set of middle failure subsets firstly. And for each subset, the conventional line sampling can be used to obtain the corresponding value of the response's CDF. At the same time, the sensitivity estimations of each failure subset can also be computed by modifying the important direction and corresponding reliability coefficients. The properties of CDF sensitivity are proved while the performance function is linear with normal random variables. After two simple examples are used to demonstrate the properties of CDF sensitivity and the feasibility of the presented method, the method employed to analyze the CDF and corresponding sensitivity of root bending moment (RBM) responses for the stochastic BAH is wing with gust excitation to a square-edged gust and to a Dryden gust. The results show that the parameters of the second and the fifth order modals exert more influence on the CDF of response than the other ones, and the presented SLS method can more significantly reduce the computational cost compared with Monte Carlo simulation (MCS).

A novel method for importance measure analysis in the presence of epistemic and aleatory uncertainties

For structural systems with both epistemic and aleatory uncertainties, research on quantifying the contribution of the epistemic and aleatory uncertainties to the failure probability of the systems is conducted. Based on the method of separating epistemic and aleatory uncertainties in a variable, the core idea of the research is firstly to establish a novel deterministic transition model for auxiliary variables, distribution parameters, random variables, failure probability, then to propose the improved importance sampling(IS) to solve the transition model. Furthermore,the distribution parameters and auxiliary variables are sampled simultaneously and independently;therefore, the inefficient sampling procedure with an ‘‘inner-loop'' for epistemic uncertainty and an‘‘outer-loop'' for aleatory uncertainty in traditional methods is avoided. Since the proposed method combines the fast convergence of the proper estimates and searches failure samples in the interesting regions with high efficiency, the proposed method is more efficient than traditional methods for the variance-based failure probability sensitivity measures in the presence of epistemic and aleatory uncertainties. Two numerical examples and one engineering example are introduced for demonstrating the efficiency and precision of the proposed method for structural systems with both epistemic and aleatory uncertainties.

Revised regional importance measures in the presence of epistemic and aleatory uncertainties

Two revised regional importance measures(RIMs),that is,revised contribution to variance of sample mean(RCVSM)and revised contribution to variance of sample variance(RCVSV),are defined herein by using the revised means of sample mean and sample variance,which vary with the reduced range of the epistemic parameter.The RCVSM and RCVSV can be computed by the same set of samples,thus no extra computational cost is introduced with respect to the computations of CVSM and CVSV.From the plots of RCVSM and RCVSV,accurate quantitative information on variance reductions of sample mean and sample variance can be read because of reduced upper bound of the range of the epistemic parameter.For general form of quadratic polynomial output,the analytical solutions of the original and the revised RIMs are given.Numerical example is employed and results demonstrate that the analytical results are consistent and accurate.An engineering example is applied to testify the validity and rationality of the revised RIMs,which can give instructions to the engineers about how to reduce variance of sample mean and sample variance by reducing the range of epistemic parameters.