Abstract. In this paper, the finite-time synchronization problem for chaotic Cohen–Grossberg neural networks with unknown parameters and time-varying delays is investigated by using finitetime stability theory. Firstly, based on the parameter identification of uncertain delayed neural networks, a simple and effective feedback control scheme is proposed to tackle the unknown parameters of the ad...

In this paper, two new identification schemes based on VSS theory and sliding mode are developed to address the time-varying parameter identification problems. The proposed direct identification scheme can successfully estimate fast time-varying parameters when the process is linear in the parametric space. The proposed indirect identification scheme further exploits the possibility of estimati...

In this paper we propose a joint space-Doppler parameter estimation algorithm for fast-fading multipath DS-CDMA communication systems. The Doppler shifts induced by the relative motion between the transmitter and the receiver, together with the associated direction-of-arrival (DOA), are jointly estimated by a semi-blind subspace-type method. The Doppler effect is successfully eliminated by a sp...

In this article we study the existence of almost periodic solutions for distributed parameters biochemical system, with time delay when the input Sin is time dependent. This study is motivated by the input begin time dependent in many applications, and by the importance of almost periodically varying environments. Using the semigroup method, we prove that if the input is almost periodic then th...

In this paper, we analyze the effects of oil revenue shocks on different sectors of the Iranian economy. We use quarterly data of the Iranian economy from 1988:2 to 2011:1 to analyze a time-varying parameter VAR model with the Bayesian method. The results show that in the late 1980s and early 1990s, the positive effects of oil revenue were mostly emerged in the industrial and oil sectors, havin...