Application of particle swarm optimization to ultra-wideband multistatic radar used for protection of indoor environment

2007 ◽  
Author(s):  
Rubem G. Farias ◽  
Victor Dmitriev ◽  
e Rodrigo M. de Oliveira
Keyword(s):  
Particle Swarm Optimization ◽  
Indoor Environment ◽  
Particle Swarm ◽  
Ultra Wideband ◽  
Swarm Optimization ◽  
Multistatic Radar

scholarly journals Joint Optimization of Microstrip Patch Antennas Using Particle Swarm Optimization for UWB Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Zubair ◽  
Muhammad Moinuddin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Monopole Antenna ◽  
Ultra Wideband ◽  
Joint Optimization ◽  
Geometrical Parameters ◽  
Swarm Optimization ◽  
Physical Constraints ◽  
High Data

Ultra wideband (UWB) systems are the most appropriate for high data rate wireless transmission with low power consumption. However, the antenna design for UWB has been a challenging task. Moreover, it is always desirable to have more freedom by designing different shape antennas with identical characteristics so that they can be used in either transmitter or receiver depending on other physical constraints such as area. To tackle these issues, in this paper, we have investigated a joint optimization of three different shape-printed monopole antennas, namely, printed square monopole antenna, printed circular monopole antenna and printed hexagonal monopole antenna, for UWB applications. More specifically, we have obtained the optimized geometrical parameters of these antennas by minimizing the mean-square-error for desired lower band edge frequency, quality factor, and bandwidth. The objective of joint optimization is to have identical frequency characteristics for the aforementioned three types of PMA which will give a freedom to interchangeably use them at either side, transmitting or receiving. Moreover, we employ particle swarm optimization (PSO) algorithm for our problem as it is well known in the literature that PSO performs well in electromagnetic and antenna applications. Simulation results are presented to show the performance of the proposed design.

Hyperparameters optimization of neural network using improved particle swarm optimization for modeling of electromagnetic inverse problems

2021 ◽  
pp. 1-12
Author(s):  
Debanjali Sarkar ◽  
Taimoor Khan ◽  
Fazal Ahmed Talukdar
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Particle Swarm ◽  
Ultra Wideband ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ann Models

Abstract Optimization of hyperparameters of artificial neural network (ANN) usually involves a trial and error approach which is not only computationally expensive but also fails to predict a near-optimal solution most of the time. To design a better optimized ANN model, evolutionary algorithms are widely utilized to determine hyperparameters. This work proposes hyperparameters optimization of the ANN model using an improved particle swarm optimization (IPSO) algorithm. The different ANN hyperparameters considered are a number of hidden layers, neurons in each hidden layer, activation function, and training function. The proposed technique is validated using inverse modeling of two meander line electromagnetic bandgap unit cells and a slotted ultra-wideband antenna loaded with EBG structures. Three other evolutionary algorithms viz. hybrid PSO, conventional PSO, and genetic algorithm are also adopted for the hyperparameter optimization of the ANN models for comparative analysis. Performances of all the models are evaluated using quantitative assessment parameters viz. mean square error, mean absolute percentage deviation, and coefficient of determination (R2). The comparative investigation establishes the accurate and efficient prediction capability of the ANN models tuned using IPSO compared to other evolutionary algorithms.

scholarly journals Particle swarm optimization–based minimum residual algorithm for mobile robot localization in indoor environment

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141772927 ◽  
Author(s):  
Yunzhou Zhang ◽  
Hang Hu ◽  
Wenyan Fu ◽  
Hao Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Mobile Robot ◽  
Indoor Environment ◽  
Particle Swarm ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Robot Localization ◽  
Received Signal Strength Indicator ◽  
Swarm Optimization ◽  
Minimum Residual

For indoor mobile robots, many localization systems based on wireless sensor network have been reported. Received signal strength indicator is often used for distance measurement. However, the value of received signal strength indicator always has large fluctuation because radio signal is easily influenced by environmental factors. This will bring adverse effect on the distance measurement and deteriorate the performance of robot localization. In this article, the measured data are dealt with weighted recursive filter, which can depress the measurement noise effectively. In the linearization procedure, the least square method often causes additional error because it seriously relies on anchor nodes. Therefore, a minimum residual localization algorithm based on particle swarm optimization is proposed for a mobile robot running in indoor environment. With continuous optimization and update of particle swarm, the position that gets the best solution of objective function can be adopted as the final estimated position. Experiment results show that the proposed algorithm, compared with traditional algorithms, can attain better localization accuracy and is closer to Cramer–Rao lower bound.

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