• Journal of Information Processing Systems
• /
• v.14 no.2
• /
• pp.455-468
• /
• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.4
• /
• pp.1479-1501
• /
• 2020

Dependable functioning of dynamic networks is essential for delivering ubiquitous services. Faults are the root causes of network outages. The comparison diagnosis model, which automates fault's identification, is one of the leading approaches to attain network dependability. Most of the existing research has focused on stationary networks. Nonetheless, the time-free comparison model imposes no time constraints on the system under considerations, and it suits most of the diagnosis requirements of dynamic networks. This paper presents a novel protocol that diagnoses faulty nodes in diagnosable dynamic networks. The proposed protocol comprises two stages, a testing stage, which uses the time-free comparison model to diagnose faulty neighbour nodes, and a disseminating stage, which leverages a Random Linear Network Coding (RLNC) technique to disseminate the partial view of nodes. We analysed and evaluated the performance of the proposed protocol under various scenarios, considering two metrics: communication overhead and diagnosis time. The simulation results revealed that the proposed protocol diagnoses different types of faults in dynamic networks. Compared with most related protocols, our proposed protocol has very low communication overhead and diagnosis time. These results demonstrated that the proposed protocol is energy-efficient, scalable, and robust.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.4
• /
• pp.236-241
• /
• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.4
• /
• pp.242-248
• /
• 2007

This paper analyzes on the chaos characteristics of the chaotic neural networks and presents the convergence condition. Although the transient chaos of neural network sould be beneficial to overcome the local minimum problem and speed up the learning, the permanent chaotic response gives adverse effect on optimization problems and makes neural network unstable in general. This paper investigates the dynamic characteristics of the chaotic neural networks with the chaotic dynamic neuron, and presents the convergence condition for stabilizing the chaotic neural networks.

• Journal of the Korea Society for Simulation
• /
• v.14 no.1
• /
• pp.33-42
• /
• 2005

Service Level Agreement (SLA) is a service providing scheme by a service class agreement between a user and a service provider SLA allows that a user can select an expected service class in various service classes provided from a service provider. Recently, SLA management is adapted to support the end-to-end Qos for service users in Beyond 3 Generation (B3G) networks. In B3G networks, SLA provides multiple service classes on access networks so that service classes should be managed to assure the service satisfaction for users. In this paper, we propose a dynamic Qos management scheme by IP traffic class controlling based on SLA in B3G networks. Also, to manage dynamic traffic service, we consider Differentiated services (Diffserv) mechanism for the resource management by SLA. An If service traffic class on SLA can be dynamically changed by Diffserv traffic management to support dynamic end-to-end Qos. Hence, in this paper, we consider the buffer threshold scheme for controlling traffic loads and the traffic level control scheme for implementing the dynamic traffic management by the SLA.

• ETRI Journal
• /
• v.32 no.2
• /
• pp.292-301
• /
• 2010

Increasing convergence among heterogeneous radio networks is expected to be a key feature of future ubiquitous services. The convergence of radio networks in combination with dynamic spectrum allocation (DSA) could be a beneficial means to solve the growing demand for radio spectrum. DSA might enhance the spectrum utilization of involved radio networks to comply with user requirements for high-quality multimedia services. This paper proposes a simple spectrum allocation algorithm and presents an analytical model of dynamic spectrum resource allocation between two networks using a 4-D Markov chain. We argue that there may exist a break-even point for choosing whether or not to adopt DSA in a system. We point out certain circumstances where DSA is not a viable alternative. We also discuss the performance of DSA against the degree of resource sharing using the proposed analytical model and simulations. The presented analytical model is not restricted to DSA, and can be applied to a general resource sharing study.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.1-10
• /
• 2021

In recent decades, the heterogeneous and dynamic behavior of Internet traffic has placed new demands on the adaptive resource allocation of the optical network infrastructure. However, the advent of multifiber elastic optical networks has led to a higher degree of spectrum fragmentation than conventional flexible grid networks due to the dynamic and random establishment and removal of optical connections. In this paper, we propose heuristic routing and dynamic slot allocation algorithms to minimize spectrum fragmentation and reduce the probability of blocking future connection requests by considering the power consumption in elastic multifiber elastic optical networks.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.3
• /
• pp.41-48
• /
• 2003

This paper presents an indirect adaptive neuro controller using modified chaotic neural networks(MCNN) for nonlinear dynamic system. A modified chaotic neural networks model is presented for simplifying the traditional chaotic neural networks and enforcing dynamic characteristics. A new Dynamic Backpropagation learning method is also developed. The proposed MCNN paradigm is applied to the system identification of a MIMO system and the indirect adaptive neuro controller. The simulation results show good performances, since the MCNN has robust adaptability to nonlinear dynamic system.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.3
• /
• pp.1373-1379
• /
• 2011

The SLA (Service Level Agreement) is once determined between service provider and users in B3G networks so that the network service must be provided as QoS management. Here, the network situation is changed as the time flow, and the service environment is also altered. Hence, dynamic QoS management scheme should be considered. In this paper, we propose a handover scheme for dyanamic QoS as the network situation in B3G networks. The heterogeneous networks can be used for dynamic QoS management in B3G networks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.413-416
• /
• 1997

Recently, dynamic recurrent neural networks(DRNN) for identification of nonlinear dynamic systems have been researched extensively. In general, dynamic backpropagation was used to adjust the weights of neural networks. But, this method requires many complex calculations and has the possibility of falling into a local minimum. So, we propose a new approach to identify nonlinear dynamic systems using DRNN. In order to adjust the weights of neurons, we use evolution strategies, which is a method used to solve an optimal problem having many local minimums. DRNN trained by evolution strategies with mutation as the main operator can act as a plant emulator. And the fitness function of evolution strategies is based on the difference of the plant's outputs and DRNN's outputs. Thus, this new approach at identifying nonlinear dynamic system, when applied to the simulation of a two-link robot manipulator, demonstrates the performance and efficiency of this proposed approach.