جستجوی مقالات مرتبط با کلیدواژه « الگوریتم زنبور » در نشریات گروه « فنی و مهندسی »

Automatic modulation recognition of digital signals is an essential for intelligent communication systems. Most automatic classifications of digital signal types deal with recognizing signals formats in presence of additive white Gaussian noise (AWGN) in channels. However, real world communication environments, such as wireless communication channels, suffer from fading effects. There are few methods proposed to perform in fading channels. This paper presents a high efficient method for identification of M-array phase shift keying (MPSK) digital signal type. The proposed method is heuristic hybrid, formed by a multilayer perceptron (MLP) neural network as the classifier and the bees algorithm (BA) as the optimizer. An equalizer is also used to reduce channel effects. A suitable combination of higher order statistics, up to eighth, is considered as prominent characteristics of signals. Simulation results validate the high efficiency of the proposed technique in recognizing the types of digital signals even at low SNRs.

Due to the strategic state of the naval forces in the country and the need to cope with today's advances in submarine technology, synchronous permanent magnet electric motors for use in unmanned underwater units, have been more applicable in recent years. Most of the prominent electrical companies therefore, have triggered several researchers in this field. For example, replacement of electromechanical or electro hydrostatical drivers by hydraulic and pneumatic drivers to reduce weight, cost of maintenance, and heighten power density, and efficiency have attracted many attentions. In this paper design optimization of three-phase permanent magnet synchronous motors applicable in unmanned submarines is discussed. For this purpose, characteristics of a motor used in unmanned submarines are investigated and then equations related to design are evaluated. Then motor is optimized using optimization Bees algorithm (BA) to have less volume and best efficiency and power density.

In this paper, an intelligent robust controller is proposed for a class of nonlinear systems in presence of uncertainties and bounded external disturbances. The proposed method is based on a combination of terminal sliding mode control and adaptive neuro-fuzzy inference system with bee’s algorithm training. For this purpose, a sliding surface is firstly designed based on terminal sliding control method. This sliding surface is considered as input for the intelligent controller which is an adaptive neuro-fuzzy inference system and using it, terminal sliding mode control law without the switching part is approximated. In the proposed method, an intelligent bee’s algorithm is also used for updating the weights of the adaptive neuro-fuzzy inference system. Compared with fast terminal sliding mode control, the proposed controller provides advantages such as robustness against uncertainty and disturbance, simplicity of controller structure, higher convergence speed compared with similar conventional methods and chattering-free control effort. The method is applied to an atomic force microscope for nano manipulation. The simulation results show the robustness and effectiveness of the proposed method.

In this paper, an intelligent integral terminal sliding mode control method with a new sliding surface is proposed based on adaptive neural-fuzzy inference. First, a terminal sliding mode controller using a novel sliding surface is designed based on Lyapunov’s stability theorem for controlling a class of chaotic systems in presence of uncertainty and disturbance. The proposed sliding surface is a combination of the conventional terminal sliding surface and integral of a nonlinear function of the states of the system. The purpose of choosing this surface includes achieving appropriate response speed, removing chattering and robustness against external disturbance. Then, we assume that a nonlinear part of the system is unknown and only input-output data is available. Therefore, an adaptive neuro-fuzzy inference system is used to approximate the unknown part of the system dynamics. Finally, in order to enhance the performance of the proposed method, the honey bee algorithm is utilizd for selecting the coefficients of integral terminal sliding mode controller. The simulation results show the effectiveness of the controller due to the improved speed, removed chattering, appropriate transient response and satisfactory performance in the presence of uncertainties in the system model.

Tourism is one of human activities that have strong cultural، social and economic effects on societies. One of the concerns of the tourist is how to select the order and the path to the tourism centers he/she wants to visit. Information such as the distances between tourist attractions، traffic and the start location of the tourist should be considered when deciding about the travel path of the tourist. The goal of tourist path finding is to find a path، through which the tourist can visit all attractions in shortest possible time، considering his priorities، traffic and the distances between attractions. This can be assumed as a spatial optimization problem with such a wide search space that cannot be solved using mathematical and deterministic methods. The goal of this research is to solve this problem using Artificial Bee Colony (ABC) meta-heuristic algorithm. The simulated data of a city with 10 tourist attraction is used for the implementation. At the 350th run of the ABC algorithm the acceptable results are gained. Taking into account the implementation speed، convergence speed and pattern، and the quality of the answers، the algorithm can be accepted as a suitable solution to the tourist path finding problem.

Abstract: Brushless permanent magnet motors play an important role in many applications. Power density and efficiency are two important factors in designing such motors. This paper proposes a novel approach to design a brushless permanent magnet motor based on optimization of a combination of power density and efficiency. First of all, this paper presents the equation related to the design and dimensions of brushless permanent magnet motor. Then, an optimum design based on bees algorithm (BA) with the purpose of increasing power density is presented. Finally, Simulation results of a 2-D finite element analysis have well validated the efficiency of the applied method.