An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context

In an evidential reasoning context, a group consensus (GC) based approach can model multiple attributive group decision analysis problems with GC requirements. The predefined GC is reached through several rounds of group analysis and discussion (GAD) in the approach. However, the GAD with no guidance may not be the most appropriate way to reach the predefined GC because several rounds of GAD will spend a lot of time of all experts and yet cannot help them to effectively emphasize on the assessments which primarily damage the GC. In this paper, an attribute weight based feedback model is constructed to effectively identify the assessments primarily damaging the GC and accelerate the GC convergence. Considering important attributes with the weights more than or at least equal to the mean of the weights of all attributes, the feedback model constructs identification rules to identify the assessments damaging the GC for the experts to renew. In addition, a suggestion rule is introduced to generate appropriate recommendations for the experts to renew their identified assessments. The identification rules are constructed at three levels including the attribute, alternative and global levels. The feedback model is used to solve an engineering project management software selection problem to demonstrate its detailed implementation process, its validity and applicability, and its advantages compared with the GC based approach.     

Towards a unified recurrent neural network theory: The uniformly pseudo-projection-anti-monotone net

In the past decades, various neural network models have been developed for modeling the behavior of human brain or performing problem-solving through simulating the behavior of human brain. The recurrent neural networks are the type of neural networks to model or simulate associative memory behavior of human being. A recurrent neural network (RNN) can be generally formalized as a dynamic system associated with two fundamental operators: one is the nonlinear activation operator deduced from the input-output properties of the involved neurons, and the other is the synaptic connections (a matrix) among the neurons. Through carefully examining properties of various activation functions used, we introduce a novel type of monotone operators, the uniformly pseudo-projectionanti-monotone (UPPAM) operators, to unify the various RNN models appeared in the literature. We develop a unified encoding and stability theory for the UPPAM network model when the time is discrete. The established model and theory not only unify but also jointly generalize the most known results of RNNs. The approach has lunched a visible step towards establishment of a unified mathematical theory of recurrent neural networks.     

Examining students’ variational reasoning in differential equations

In this study, we explored how a sample of eight students used variational reasoning while discussing ordinary differential equations (DEs). Our analysis of variational reasoning draws on the literature with regard to student thinking about derivatives and rate, students’ covariational reasoning, and different multivariational structures that can exist between multiple variables. First, we found that while students can think of “derivative” as a variable in and of itself and also unpack derivative as a rate of change between two variables, the students were often able to think of “derivative” in these two ways simultaneously in the same explanation. Second, we found that students made significant usage of covariational reasoning to imagine relationships between pairs of variables in a DE, and that mental actions pertaining to recognizing dependence/independence were especially important. Third, the students also conceptualized relationships between multiple variables in a DE that matched different multivariational structures. Fourth, importantly, we identified a type of variational reasoning, which we call “feedback variation”, that may be unique to DEs because of the recursive relationship between a function’s value and its own rate of change.     

不确定离散模糊随机系统的鲁棒方差约束输出反馈控制

对一类具有范数有界不确定性的离散T-S模糊随机系统。研究不仅使整个闭环模糊系统全局渐近稳定。而且每个模糊子系统的稳态状态方差满足给定上界性能指标约束的输出反馈鲁棒方差控制律的设计问题。利用线性矩阵不等式(LMI)技术，导出输出反馈鲁棒方差控制律的存在条件，并基于矩阵相似变换给出其可解性条件，同时用一组线性矩阵不等式的可行解。给出输出反馈鲁棒方差控制律的一个参数化表达形式。     

An active resonator system for CW-ESR measurement operating at 700 MHz

An active resonator system operating at 700 MHz, which can attain a high Q for CW-ESR measurements of a high loss sample, was developed. This system consisted of a loop-gap resonator (LGR), a receiver coil, an excitation coil, and a phase tunable amplifier. A part of the RF power at the LGR was picked up by the receiver coil, amplified, and irradiated to the LGR again by the excitation coil, which made up a feedback circuit. Because the feedback circuit provided the energy that canceled the loss in the resonator, the Q of the active resonator system increased. When a sample tube (inner diameter, 20 mm; axial length, 31 mm) containing a nitroxide radical and physiological saline solution was placed in the resonator, the Q could be varied from 55 to 4000. It was possible to obtain a Q of the active resonator system with sample that was higher than that of the value of the LGR without a sample in a no-feedback condition. The ESR signal intensity increased up to 7 times with the increase in Q. The sensitivity increased up to 50%, which was a much smaller advance than that of the Q, because the noise level also increased with the increase in signal intensity.     

Robust Equilibria in Indefinite Linear-Quadratic Differential Games

Equilibria in dynamic games are formulated often under the assumption that the players have full knowledge of the dynamics to which they are subject. Here, we formulate equilibria in which players are looking for robustness and take model uncertainty explicitly into account in their decisions. Specifically, we consider feedback Nash equilibria in indefinite linear-quadratic differential games on an infinite time horizon. Model uncertainty is represented by a malevolent input which is subject to a cost penalty or to a direct bound. We derive conditions for the existence of robust equilibria in terms of solutions of sets of algebraic Riccati equations.     

Bang-bang control of a plant with one positive real pole and one negative real pole

In this paper, a relay-controlled plant, possessing one positive real pole and one negative real pole, is studied. First, a regulating system is considered; we investigate the design of the controller for simultaneous reduction of error and error derivative to zero. It is seen that this can be achieved provided the initial values of error and error derivative fall inside an admissible (or controllable) region. Attention is then focused on the tracking system. It is shown that, provided the input to the system is of a form similar to the impulse response of the plant, and provided the initial values of error and error derivative of the system fall inside the same admissible region, then the controller used for regulating the system can also be used for tracking the system so as to force the output of the plant to follow the input perfectly; in both cases with at mostone switching reversal of the relay.     

A note on the regularity of solutions of infinite dimensional Riccati equations

This note is concerned with the regularity of solutions of algebraic Riccati equations arising from infinite dimensional LQR control problems. We show that distributed parameter systems described by certain parabolic partial differential equations often have a special structure that smooths solutions of the corresponding Riccati equation. This analysis is motivated by the need to find specific representations for Riccati operators that can be used in the development of computational schemes for problems where the input and output operators are not Hilbert-Schmidt. This situation occurs in many boundary control problems and in certain distributed control problems associated with optimal sensor/actuator placement.     

Biological applications of scanning electrochemical microscopy: chemical imaging of single living cells and beyond

Recent applications of scanning electrochemical microscopy (SECM) to studies of single biological cells are reviewed. This scanning probe microscopic technique allows the imaging of an individual cell on the basis of not only its surface topography but also such cellular activities as photosynthesis, respiration, electron transfer, single vesicular exocytosis and membrane transport. The operational principles of SECM are also introduced in the context of these biological applications. Recent progress in techniques for high-resolution SECM imaging are also reviewed. Future directions, such as single-channel detection by SECM, high-resolution imaging with nanometer-sized probes, and combined SECM techniques for multidimensional imaging are also discussed.     

A Priori Feedback Estimates for Multiscale Reaction-Diffusion Systems

We study the approximation of a multiscale reaction–diffusion system posed on both macroscopic and microscopic space scales. The coupling between the scales is done through micro–macro flux conditions. Our target system has a typical structure for reaction–diffusion flow problems in media with distributed microstructures (also called, double porosity materials). Besides ensuring basic estimates for the convergence of two-scale semi-discrete Galerkin approximations, we provide a set of a priori feedback estimates and a local feedback error estimator that help in designing a distributed-high-errors strategy to allow for a computationally e?cient zooming in and out from microscopic structures. The error control on the feedback estimates relies on two-scale-energy, regularity, and interpolation estimates as well as on a fine bookeeping of the sources responsible with the propagation of the (multiscale) approximation errors. The working technique based on a priori feedback estimates is in principle applicable to a large class of systems of PDEs with dual structure admitting strong solutions.