Sakhinah Abu Bakar

Projective lag synchronization in drive-response dynamical networks

This paper investigates projective lag synchronization (PLS) behavior between chaotic systems in drive-response dynamical networks (DRDNs) model with nonidentical nodes. A hybrid feedback control method is designed to achieve the PLS with and without mismatched terms. Specially, the coupling matrix in this model is not assumed to be symmetric, diffusive or irreducible. The stability of the error dynamics is proven theoretically using the Lyapunov stability theory. Finally, analytical results show that the states of the dynamical network with non-delayed coupling can be asymptotically synchronized onto a desired scaling factor under the designed controller. Moreover, the numerical simulations results demonstrate the validity of the proposed method.

Adaptive projective lag synchronization of uncertain complex dynamical networks with disturbance

Projective lag synchronization behavior with non-delay and delay coupling in complex dynamical network model is investigated in this paper. Based on Lyapunov stability theory, adaptive control scheme is applied to achieve the projective lag synchronization model with identical, different nodes and even if the delay coupling have constant time delay or time-varying coupling delay. In addition, the model consists of disturbances and fully unknown parameters. Theses parameters are identified by adaptive control and update law. Finally, the simulation results reveal that the states of the dynamical network with non-delay and delay coupling can be asymptotically synchronized onto a desired scaling under the designed controller. Additionally, numerical examples demonstrate the effectiveness of the proposed method.

Characterization of essential proteins based on network topology in proteins interaction networks

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our st...

Hybrid support vector regression in electric load during national holiday season

This paper studies non-parametric time-series approach to electric load in national holiday seasons based on historical hourly data in state electric company of Indonesia consisting of historical data of the Northern Sumatera also South and Central Sumatra electricity load. Given a baseline for forecasting performance, we apply our hybrid models and computation platform with combining parameter of the kernel. To facilitate comparison to results of our analysis, we highlighted the results around MAPE-based and R2-based techniques. In order to get more accurate results, we need to improve, investigate, also develop the appropriate statistical tools. Electric load forecasting is a fundamental aspect of infrastructure development decisions and can reduce the energy usage of the nation.

Hybrid function projective synchronization in discrete dynamical networks via adaptive control

In this paper, we study the hybrid function projective synchronization between coupled complex discrete networks with different dimensions. The hybrid function projective synchronization is achieved by designing an adaptive control method. Based on the designed controller and the Lyapunov stability theory, we derive sufficient conditions to realize the hybrid function projective synchronization with different nodes. Moreover, with the adaptive update law, an adaptive control gains are obtained. Furthermore, we examine different cases of outer coupling matrix of node dynamics. Finally, we provide numerical examples to show the effectiveness of the proposed control scheme. c ©2017 All rights reserved.

Adaptive hybrid function projective synchronization in drive-response discrete networks with delay coupling

This paper investigates hybrid function projective synchronization behavior between chaotic and hyperchaotic systems in drive-response discrete networks model with different nodes and delay coupling. Using Lyapunov stability theory, an adaptive control scheme is applied to achieve hybrid function projective synchronization with different order. Based on adaptive update law, adaptive control gains are designed. Finally, numerical examples demonstrate the effectiveness of the proposed method.

Projective lag synchronization in drive-response dynamical networks via hybrid feedback control

This paper investigates projective lag synchronization (PLS) behavior in drive-response dynamical networks (DRDNs) model with non-identical reference node. Based on Lyapunov stability theory and hybrid feedback control method the problem of PLS with mismatch terms is solved. Finally, analytical results show that the states of the dynamical network with non-delayed coupling can be asymptotically synchronized onto a desired scaling factor under the designed controller. Moreover, the numerical simulations results demonstrate the validity of the proposed method.