Narrow Results

It is natural to seek a measure for assessing the influence of individual component within a system. In this paper, we define the influence index of uncertain systems and introduce several key properties of this index. Furthermore, we investigate the influence indexes of specific types of uncertain reliability systems, including uncertain series, parallel, parallel-series, and series-parallel configurations. Some formulas are also derived to calculate the influence index of a component in uncertain Boolean systems.

In this paper the regulation problem of linear discrete-time systems with uncertain parameters under state and control constraints is studied. In the first part of the paper, two theorems concerning necessary and sufficient conditions for the existence of a solution to this problem are presented. Due to the constructive form of the proof of these theorems, these results can be used to the development of techniques for the derivation of a control law transferring to the origin any state belonging to a given set of initial states while respecting the state and control constraints.

The design of fuzzy cognitive maps (FCMs) mainly relies on human knowledge, which implies subjectivity of the developed model. This affects the accuracy of an FCM significantly. In order to address this issue, we propose a novel learning model for FCMs in this paper. It achieves efficient learning by automatically adjusting the system parameters according to the environment. The learning model consists of extreme learning machine (ELM) and a curious model, where ELM learns from the modeled system and the curious model helps to further improve the performance of ELM. We use an example to illustrate the effectiveness of our model. The simulation results show that our model helps to improve the accuracy of FCMs.

The topic of this paper is related to the inverse stochastic mechanic problems, in which it is necessary to estimate the unknown mechanical and geometrical random quantities of the structures once the external loads and displacement responses are probabilistically known. In particular, in this work, a probability-based approach for inverse stochastic problems, working directly in terms of probability density functions (PDFs), is presented. This approach has been possible thanks to the application of the probability transformation method (PTM), which has been recently introduced for the solution of many stochastic analysis problems.

The problem of design of exponential stability non-fragile control for uncertain systems with time-varying delay is considered in this paper. Based on the Lyapunov stability theorem, and by using linear matrix inequality approach, a new approach is obtained to design the state feedback exponential stability non-fragile controller. By introducing a new Lyapunov functional, a sufficient exponential stability condition is given in terms of linear matrix inequality. With the non-fragile controller and the linear matrix inequality Control Toolbox in MATLAB, the simulation results are easier obtained.

In this paper we study the problem of semiglobally stabilizing uncertain nonlinear systems via measurement feedback, characterized by a nominal linear part and (measurable) uncertain nonlinear terms, which are known only up to some nonlinear bounds. Our result recovers and generalizes well-known results for the case of uncorrupted outputs and input saturations