Risk-sensitive probability for Markov chains

The probability distribution of a Markov chain is viewed as the information state of an additive optimization problem. This optimization problem is then generalized to a product form whose information state gives rise to a generalized notion of probability distribution for Markov chains. The evolution and the asymptotic behavior of this generalized or “risk-sensitive” probability distribution is studied in this paper and a conjecture is proposed regarding the asymptotic periodicity of risk-sensitive probability and proved in the two-dimensional case. The relation between a set of simultaneous non-linear and the set of periodic attractors is analyzed.     

On the asymptotic stability of coupled delay differential and continuous time difference equations

A two-step Liapunov-Krasovskii methodology for checking the asymptotic stability of nonlinear coupled delay differential and continuous time difference equations is proposed here. The feasibility of such methodology is shown by means of Liapunov-Krasovskii functionals with nonconstant kernels in the integrals, for instance discretized Liapunov-Krasovskii ones. An illustrative example taken from the literature, showing the effectiveness of the proposed method, is reported.     

Investigation of singularity orders and eigen-angular functions for V-notches in radially inhomogeneous materials

The singularities of V-notches in a material whose modulus of elasticity varies as a power function of the r-coordinate are investigated. The eigen-equations are derived by substituting the asymptotic expansions of displacement fields around a notch vertex into the elasticity equilibrium equations. The radial boundary conditions are also presented by a combination of stress singularity orders and eigen-angular functions. The singularity orders and eigen-angular functions can be calculated simultaneously by solving a set of eigen ordinary differential equations with variable coefficients, which are solved by the interpolating matrix method developed by some of the authors before without the iterative process. The accuracy of the proposed method is verified by comparing the present results with the reference ones when the inhomogeneous material is degraded into a homogeneous one. Then, the stress singularities of V-notches in the radially inhomogeneous material under the plane and anti-plane loadings are investigated, respectively. The results show that the stress singularity of a V-notch in the radially inhomogeneous material under the plane loading is more serious than the one under the anti-plane loading. The plane V-notch under the clamped–clamped boundary condition presents the stress singularity at a smaller notch angle α than the one under the free–free boundary condition. The radially inhomogeneous bulgy V-notch even presents singularity. In addition, the stress singularity becomes stronger with the increase of the exponent c in the variation function of the elasticity modulus.     

On the Performance of Phase‐I Bivariate Dispersion Charts to Non‐Normality

A phase‐I study is generally used when population parameters are unknown. The performance of any phase‐II chart depends on the preciseness of the control limits obtained from the phase‐I analysis. The performance of phase‐I bivariate dispersion charts has mainly been investigated for bivariate normal distribution. However, this assumption is seldom fulfilled in reality. The current work develops and studies the performance of phase‐I |S| and |G| charts for monitoring the process dispersion of bivariate non‐normal distributions. The necessary control charting constants are determined for the bivariate non‐normal distributions at nominal false alarm probability (FAP₀). The performance of these charts is evaluated and compared in a situation when samples are generated by bivariate logistic, bivariate Laplace, bivariate exponential, or bivariate t₅ distribution. The analysis shows that the proper consideration to underlying bivariate distribution in the construction of phase‐I bivariate dispersion charts is very important to give a real picture of in or out of control process status. Copyright © 2016 John Wiley & Sons, Ltd.     

Pairwise Estimation of Multivariate Gaussian Process Models With Replicated Observations: Application to Multivariate Profile Monitoring

Profile monitoring is often conducted when the product quality is characterized by profiles. Although existing methods almost exclusively deal with univariate profiles, observations of multivariate profile data are increasingly encountered in practice. These data are seldom analyzed in the area of statistical process control due to lack of effective modeling tools. In this article, we propose to analyze them using the multivariate Gaussian process model, which offers a natural way to accommodate both within-profile and between-profile correlations. To mitigate the prohibitively high computation in building such models, a pairwise estimation strategy is adopted. Asymptotic normality of the parameter estimates from this approach has been established. Comprehensive simulation studies are conducted. In the case study, the method has been demonstrated using transmittance profiles from low-emittance glass. Supplementary materials for this article are available online.     

On Efficient Skewness Correction Charts Under Contamination and Non‐normality

Shewhart control charts are very popular in a variety of disciplines such as industry, agriculture and medical science. The design structure of the usual Shewhart charts depends on normality and one point decision rule. This makes the scope of these charts quite limited and not very efficient for small shifts. This study comes up with an intermediate solution by implementing runs rules schemes and adjusting the limits' coefficients for non‐normality using the idea of skewness correction. We have covered some commonly used location and dispersion charts, namely , R and S charts. We have investigated the performance of the proposals in terms of false alarm rate and signalling probability. We have observed that the proposals serve the dual purpose in terms of robustness and efficiency. The study also provides an application example using numerical dataset. Copyright © 2015 John Wiley & Sons, Ltd.     

Suboptimal mean controllers for bounded and dynamic stochastic distributions

Based on the recently developed algorithms for the modelling and control of bounded dynamic stochastic systems (H. Wang, J. Zhang, Bounded stochastic distributions control for pseudo ARMAX stochastic systems, IEEE Transactions on Automatic control, 486–490), this paper presents the design of a subotpimal nonlinear mean controller for bounded dynamic stochastic systems with guaranteed stability. The B-spline functional expansion based square root model is used to represent the output probability density function of the system. This is then followed by the design of a mean controller of the output distribution of the system using nonlinear output tracking concept. A nonlinear quadratic optimization is performed using the well known Hamilton–Jacobi–Bellman equation. This leads to a controller which consists of a static unit, a state feedback part and an equivalent output feedback loop. In order to achieve high precision for the output tracking, the output feedback gain is determined by a learning process, where the Lyapunov stability analysis is performed to show the asymptotic stability of the closed loop system under some conditions. A simulation example is included to demonstrate the use of the algorithm and encouraging results have been obtained.     

Asymptotic and oscillatory behavior of solutions of general nonlinear difference equations of second order

The asymptotic and oscillatory behavior of solutions of some general second-order nonlinear difference equations of the form

δ(a_nh(y_n+1)δy_n)+p_nδy_n+q_n+1f(y_σ(n+1))=0 nZ,

is studied. Oscillation criteria for their solutions, when “p_n” is of constant sign, are established. Results are also presented for the damped-forced equation

δ(a_nh(y_n+1)δy_n)+p_nδy_n+q_n+1f(y_σ(n+1))=e_n nZ.

Examples are inserted in the text for illustrative purposes.     

Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects remotely. For the tracking control of teleoperation systems, velocity measurements are necessary to provide feedback information. However, due to hardware technology and cost constraints, the velocity measurements are not always available. In addition, the time-varying communication delay makes it challenging to achieve tracking task. This paper provides a solution to the issue of real-time tracking for teleoperation systems, subjected to unavailable velocity signals and time-varying communication delays. In order to estimate the velocity information, immersion and invariance (I&I) technique is employed to develop an exponential stability velocity observer. For the proposed velocity observer, a linear relationship between position and observation state is constructed, through which the need of solving partial differential and certain integral equations can be avoided. Meanwhile, the mean value theorem is exploited to separate the observation error terms, and hence, all functions in our observer can be analytically expressed. With the estimated velocity information, a slave-torque feedback control law is presented. A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking conditions. In particular, the relationship between the controller design parameters and the allowable maximum delay values is provided. Finally, simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.      

Novel delay-dependent stability condition for mixed delayed stochastic neural networks with leakage delay signals

In this paper, the problem of stability condition for mixed delayed stochastic neural networks with neutral delay and leakage delay is investigated. A novel Lyapunov functional is constructed with double and triple integral terms. New sufficient conditions are derived to guarantee the global asymptotic stability of the concerned neural network. This paper is more general than the paper by Zhu et al. [Robust stability of Markovian jump stochastic neural networks with time delays in the leakage terms, Neural Process. Lett. 41 (2015), pp. 1–27]. In our paper, we considered both the neutral delay and leakage delay, but the paper by Zhu et al. is not considering the neutral delay. Also we employed triple integrals in the Lyapunov functional which is not used in the paper by Zhu et al. Finally, two numerical examples are provided to show the effectiveness of the theoretical results.