Stability and synchronization of discrete-time Markovian jumping neural networks with mixed mode-dependent time delays

Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, we introduce a new class of discrete-time neural networks (DNNs) with Markovian jumping parameters as well as mode-dependent mixed time delays (both discrete and distributed time delays). Specifically, the parameters of the DNNs are subject to the switching from one to another at different times according to a Markov chain, and the mixed time delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. We first deal with the stability analysis problem of the addressed neural networks. A special inequality is developed to account for the mixed time delays in the discrete-time setting, and a novel Lyapunov-Krasovskii functional is put forward to reflect the mode-dependent time delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the stochastic stability. We then turn to the synchronization problem among an array of identical coupled Markovian jumping neural networks with mixed mode-dependent time delays. By utilizing the Lyapunov stability theory and the Kronecker product, it is shown that the addressed synchronization problem is solvable if several LMIs are feasible. Hence, different from the commonly used matrix norm theories (such as the M-matrix method), a unified LMI approach is developed to solve the stability analysis and synchronization problems of the class of neural networks under investigation, where the LMIs can be easily solved by using the available Matlab LMI toolbox. Two numerical examples are presented to illustrate the usefulness and effectiveness of the main results obtained

High Volume Fraction Mg-based Nanocomposites: Processing, Microstructure And Mechanical Behavior

Mg-based metal matrix nanocomposites (MMNCs) with mechanical properties, superior to those of coarse-grained composites, are promising structural materials for applications in the automotive and aerospace industries. The research in this area was primarily focused earlier on either micro-scaled reinforcements or nano-scaled reinforcements with very low volume fractions. MMNCs with high volume fractions have not been explored yet. In this research, we study the processing, microstructures and properties of MMNCs containing ceramic nanoparticles up to 30 vol.%. We first investigated the mechanical alloying of Al2O3 nanoparticles and pure Mg under high-energy ball milling conditions. The phase evolution and their distribution were evaluated as a function of milling time. Then, the thermal stability of the formed nanocomposites was investigated by annealing it at high temperatures. It indicated that an exchange reaction had occurred to a large extent between Mg and Al2O3 resulting in the formation of Al and MgO phases. Additionally, the reaction between Al and unreacted Mg led to the formation of Mg-Al intermetallics. Due to the reaction between Mg and Al2O3 during the milling and annealing process, we attempted to synthesize Mg/SiC nanocomposites. The mixed powders containing 0, 5, 10 and 15 vol.% SiC were produced by high energy ball milling and then the powders were consolidated via spark plasma sintering. The phase constitutions and microstructures of the Mg/SiC nanocomposites were characterized. SiC nanoparticles (average particle size ~14 nm) appear to be homogeneously dispersed within the matrix, iv and the average inter-particle spacings of all the Mg/SiC nanocomposites were smaller than 50 nm. Microscopic methods, even at high magnifications did not reveal any significant porosity in the as-processed MMNCs. Mechanical characterization of the Mg/SiC nanocomposites was conducted using the microindentation test. Besides the microhardness test, different intermediate pause times and loading rates were used to evaluate the stiffness and loading rate sensitivity of our samples. The abnormal microhardness and loading rate sensitivity were showed for the Mg-15 vol.% SiC samples. At the same time, the monotonic increase of stiffness with volume fraction was exhibited in the Mg/SiC nanocomposites. Finally, we investigated the quasi-static and dynamic response of Mg/SiC nanocomposites and microcomposites, and discussed the underlying mechanisms. Strain softening was noticed in the milled Mg sample under quasi-static compression. Similarly, the strengthening effect leveling off was also observed in the Mg-15 vol.% SiC samples under either quasi-static or high-strain rate uniaxial compression conditions. No significant plastic deformation was observed in the Mg/SiC nanocomposites. The estimated strain rate sensitivity of all the Mg/SiC nanocomposites in this work was around 0.03, which is much smaller than 0.3 and 0.6, observed for 100 nm and 45 nm grain size pure Mg individually. In particular, the existing models fail in predicting the inverse volume fraction effect, and other mechanisms are yet to be explored. The presence of SiC nanoparticles may play an important role that leads to this differenc

Synchronization and state estimation for discrete-time complex networks with distributed delays

Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, a synchronization problem is investigated for an array of coupled complex discrete-time networks with the simultaneous presence of both the discrete and distributed time delays. The complex networks addressed which include neural and social networks as special cases are quite general. Rather than the commonly used Lipschitz-type function, a more general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. The distributed infinite time delays in the discrete-time domain are first defined. By utilizing a novel Lyapunov-Krasovskii functional and the Kronecker product, it is shown that the addressed discrete-time complex network with distributed delays is synchronized if certain linear matrix inequalities (LMIs) are feasible. The state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, for all admissible discrete and distributed delays, the dynamics of the estimation error is guaranteed to be globally asymptotically stable. Again, an LMI approach is developed for the state estimation problem. Two simulation examples are provided to show the usefulness of the proposed global synchronization and state estimation conditions. It is worth pointing out that our main results are valid even if the nominal subsystems within the network are unstable

Distributed state estimation for discrete-time sensor networks with randomly varying nonlinearities and missing measurements

Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper deals with the distributed state estimation problem for a class of sensor networks described by discrete-time stochastic systems with randomly varying nonlinearities and missing measurements. In the sensor network, there is no centralized processor capable of collecting all the measurements from the sensors, and therefore each individual sensor needs to estimate the system state based not only on its own measurement but also on its neighboring sensors' measurements according to certain topology. The stochastic Brownian motions affect both the dynamical plant and the sensor measurement outputs. The randomly varying nonlinearities and missing measurements are introduced to reflect more realistic dynamical behaviors of the sensor networks that are caused by noisy environment as well as by probabilistic communication failures. Through available output measurements from each individual sensor, we aim to design distributed state estimators to approximate the states of the networked dynamic system. Sufficient conditions are presented to guarantee the convergence of the estimation error systems for all admissible stochastic disturbances, randomly varying nonlinearities, and missing measurements. Then, the explicit expressions of individual estimators are derived to facilitate the distributed computing of state estimation from each sensor. Finally, a numerical example is given to verify the theoretical results.This work was supported in part by the Royal Society of U.K., the National Natural Science Foundation of China under Grant 60804028 and Grant 61028008, the Teaching and Research Fund for Excellent Young Teachers at Southeast University of China, the Qing Lan Project of Jiangsu Province of China, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany

State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: The discrete-time case

Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the problem of state estimation for a class of discrete-time coupled uncertain stochastic complex networks with missing measurements and time-varying delay. The parameter uncertainties are assumed to be norm-bounded and enter into both the network state and the network output. The stochastic Brownian motions affect not only the coupling term of the network but also the overall network dynamics. The nonlinear terms that satisfy the usual Lipschitz conditions exist in both the state and measurement equations. Through available output measurements described by a binary switching sequence that obeys a conditional probability distribution, we aim to design a state estimator to estimate the network states such that, for all admissible parameter uncertainties and time-varying delays, the dynamics of the estimation error is guaranteed to be globally exponentially stable in the mean square. By employing the Lyapunov functional method combined with the stochastic analysis approach, several delay-dependent criteria are established that ensure the existence of the desired estimator gains, and then the explicit expression of such estimator gains is characterized in terms of the solution to certain linear matrix inequalities (LMIs). Two numerical examples are exploited to illustrate the effectiveness of the proposed estimator design schemes

Global synchronization control of general delayed discrete-time networks with stochastic coupling and disturbances

Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the synchronization control problem is considered for two coupled discrete-time complex networks with time delays. The network under investigation is quite general to reflect the reality, where the state delays are allowed to be time varying with given lower and upper bounds, and the stochastic disturbances are assumed to be Brownian motions that affect not only the network coupling but also the overall networks. By utilizing the Lyapunov functional method combined with linear matrix inequality (LMI) techniques, we obtain several sufficient delay-dependent conditions that ensure the coupled networks to be globally exponentially synchronized in the mean square. A control law is designed to synchronize the addressed coupled complex networks in terms of certain LMIs that can be readily solved using the Matlab LMI toolbox. Two numerical examples are presented to show the validity of our theoretical analysis results.This work was supported by the Royal Society Sino-British Fellowship Trust Award of the U.K

Codimension two and three bifurcations of a predator–prey system with group defense and prey refuge

A predator–prey system with nonmonotonic functional response and prey refuge is considered. We mainly obtain that the system has the bifurcations of cusp-type codimension two and three, these illustrate that the dynamic behaviors of the model with prey refuge will become more complicated than the system with no refuge

Dc Electric Field Assitd 3ysz Ceramic Superplastic Deformation

3YSZ ceramic superplasticity has been investigated for many years, and the primary mechanism for superplastic deformation is grain boundary sliding. However, the accommodation mechanism for grain boundary sliding is controversial. In this paper, the 3YSZ ceramic was deformed in tension at different constant stress conditions. Superplastic deformation of 3YSZ ceramic was observed with the assistance of DC electric field. It indicated that at the high tensile stress condition, dislocation motion may play a role in the accommodation mechanism for grain boundary sliding

The onset of flash sintering 8YSZ

The abrupt conductivity surge of 8YSZ was achieved with applied electric field and temperature after a short incubation time during flash sintering, and accompanying fast mass transport when using green bodies. Joule heating was proposed to explain the onset of flash sintering due to the enhanced conductivity of ceramics at high temperature, however, we observed that the black front is moved from cathode to the anode side during the incubation stage in 8YSZ, and the onset is triggered when the black front almost reach the anode, but still keep a narrow gap from the electrode. It believed the association of charged oxygen vacancies with electrons induced the flash event, then the reaction of electrons and charged oxygen vacancies to form uncharged oxygen vacancy near the anode side sustained the steady state. Please click Additional Files below to see the full abstract

The Loss of Hh Responsiveness by a Non-Ciliary Gli2 Variant

Hedgehog signaling is crucial for vertebrate development and physiology. Gli2, the primary effector of Hedgehog signaling, localizes to the tip of the primary cilium, but the importance of its ciliary localization remains unclear. We address the roles of Gli2 ciliary localization by replacing endogenous Gli2 with Gli2ΔCLR, a Gli2 variant not localizing to the cilium. The resulting Gli2ΔCLRKI and Gli2ΔCLRKI;Gli3 double mutants resemble Gli2-null and Gli2;Gli3 double mutants, respectively, suggesting the lack of Gli2ΔCLR activation in development. Significantly, Gli2ΔCLR cannot be activated either by pharmacochemical activation of Smo in vitro or by loss of Ptch1 in vivo. Finally,Gli2ΔCLR exhibits strong transcriptional activator activity in the absence of Sufu, suggesting that the lack of its activation in vivo results from a specific failure in relieving the inhibitory function of Sufu. Our results provide strong evidence that the ciliary localization of Gli2 is crucial for cilium-dependent activation of Hedgehog signaling