جستجوی مقالات مرتبط با کلیدواژه "convergence" در نشریات گروه "برق"

In this paper, we simulated a diffusion adaptive network in the underwater environment. The communication method between the nodes of this network is assumed to be the visible light communication technology (VLC) which in the underwater condition is known as the UVLC. The links between the nodes in this case are contaminated with the optical noise and turbulence. These contaminations are modeled with the proper statistical distributions depending on the underwater conditions. The optical turbulence modeling link coefficients are shown to be following the Log-normal distribution which its mean and variance are directly dependent on the temperature and the salinity of the simulated water and the assumed distance between the diffusion network nodes. The performance of the diffusion network in using UVLC technology is then analyzed both with simulations and theoretical calculations and the results are presented using the steady-state error metrics. Our analysis showed that the diffusion network can be implemented underwater with the VLC technology providing that the distance between the network nodes is less than 10 meters. Also, in order to guarantee the convergence of the adaptive network, the water salinity level and temperature must not exceed the values that are presented in our simulations.

Sliding mode control is one of the most effective methods of controlling nonlinear systems with bounded uncertainty. Exponential convergence of tracking error is one of the most important problem of classic sliding mode control. One way to solve this problem is use of terminal sliding mode control. The great thing about terminal sliding mode control, is it’s robustness in face of model uncertainty and external disturbances while can guarantee tracking error converge to zero in finite time simultaneously. Usually terminal sliding mode controller, is limited by singularity at the origin and infinite control signal. This article attempts to the singularity problem in controlling underwater robots and decreasing the convergence time by defining a new sliding surface for terminal sliding mode controller. simulation results shows the efficiency of proposed controller as it effectively improves the convergence time and accuracy in under water robots with are faced by structural and environmental uncertainties.

In this paper, a reliable implicit difference scheme is proposed for analyzing the fractional fourth order sub-diffusion equation on a bounded domain. The time fractional derivative operator is characterized in the Ji Huan He’s sense and the space derivative is approximated by means of the five points centered formula. The numerical parameters, i.e., consistency, stability and convergence analysis of the considered scheme is proven.

In this letter, complex operational matrices of Euler functions and their interesting properties are obtained to provide a novel method for solving linear complex differential equations under mixed initial conditions. Convergence conditions of this method are studied in depth and numerical experiments show the efficiency of this method. In addition, reasonable numerical results are obtained by selecting small number of basis functions.

Sugarscape model is a multi-agent environment used for modeling and organizing processes such as social, political and economic processes. The purpose of this study is to assess the learning ability of a learner system in sugarscape and for that reason the Boltzmann machine learning algorithm is evaluated. During the first experience, model number 1 which is a learned multi-agent model and is based on Boltzmann machine algorithm was considered. In this model each agent, was assigned with a parameter which indicates the agent’s knowledge. Once the knowledge of agents reached the maximum, the model is converged. The criterion was time of convergence. After that, the second model which is a multi-agent, cellular, and learner based on Boltzmann machine learning algorithm was considered, in which the Boltzmann machine’s learning algorithm is implemented in a multi-agent cellular environment. And finally the third model which is a multi-agent, cellular, Boltzmann machine learner model was assessed in sugarscape. And measure the time needed to reach convergence over specific number of agents for each model. After investigating the resulting diagrams it was concluded that the convergence speed of third model is more than the convergence speed of second model. Also, the convergence speed of second model is more than convergence speed of first model. Utilization of cellular automata results in a more speedy convergence for the model and this is due to rules and local interactions and transformation of generalized convergence to localized convergences. Application of sugarscape accelerates convergence speed relative to the case when it is not used. Additionally the maximum number of executable agents in third model is more than second model and also the maximum number of executable agents in second model is much greater than the first model. At last, it was deduced that the learning process of a learned multi-agent system and in sugarscape speeds up this process and does not have the limitations in the number of agents during execution.

This paper presents a multiobjective power control algorithm that updates the transmitted power based on local information. The proposed algorithm is expanded by using multiobjective optimization schemes. The objectives to be optimized in this paper are determined so as to reduce the SINR fluctuations as well as maintaining the SINR to an acceptable level with minimizing an average transmitted power. The convergence properties of the proposed algorithm are studied theoretically and with numerical simulations. The results indicate that the algorithm converges more rapidly and has lower average transmitted power than other existing algorithms. The current study also suggests a practical version of the proposed algorithm and compares it to the existing totally distributed bang-bang power control (B-BPC) or fixed step power control (FSPC) and multiobjective totally distributed power control (MOTDPC) algorithms. Numerical results show that the proposed algorithm is potentially much more efficient in terms of convergence speed and average consumption power than the other two algorithms.