Microwave Circuit Design

2012 ◽  
pp. 18-39
Author(s):  
Gabriel Cormier ◽  
Tyler Ross
Keyword(s):  
Particle Swarm Optimization ◽  
Global Optimum ◽  
Model Parameters ◽  
Microwave Circuit ◽  
Small Signal ◽  
Signal Model ◽  
Swarm Optimization ◽  
High Frequencies ◽  
Design And Optimization ◽  
Simulation Results

Circuit models play an important role in the design and optimization of microwave circuits (circuits in the GHz frequency range). These circuit models contain many parameters, including parasitic elements, necessary to correctly model the behavior of transistors at high frequencies. These models are often designed based on a series of measurements. Because of its ability to efficiently locate the global optimum of an objective function, particle swarm optimization (PSO) can be a useful tool when matching a model to its measurements. In this chapter, PSO will be used to calculate a transistor’s small-signal model parameters, determine the noise parameters of the transistor, and design a microwave mixer. The mixer is designed at 39.25 GHz, and a comparison between measurements and simulation results shows good agreement.

Analysis and Design of E-Shaped Dual-Frequency Microstrip Antenna Based on CPSO Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 487-491
Author(s):  
Wei Ao ◽  
Wan Qin Xiang ◽  
Chun Ming Chen ◽  
Wei Tian ◽  
De Bin Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Microstrip Antenna ◽  
Dual Frequency ◽  
Wide Bandwidth ◽  
Swarm Optimization ◽  
Design And Optimization ◽  
Analysis And Design ◽  
Optimized Model ◽  
Simulation Results ◽  
Chaotic Particle Swarm Optimization

In order to solve the problem of design and optimization for E-shaped patch microstrip antenna, chaotic particle swarm optimization (CPSO) algorithm was proposed to assist the procedure. First, the E-shaped antennas model was created, and then, the parameters of this antenna were adjusted according to the CPSO algorithm, and the procedure was repeated until the antennas capabilities meet the requirements. The simulation results showed that, the optimized model of such antenna, was capable of dual-frequency operation in 1.8GHz and 2.4GHz, and it was also capable of wide bandwidth, could meet the requirements very well.

Study on Vehicle Logistics Routing Path Problem with Time Windows Based on Particle Swarm Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 5161-5164
Author(s):  
Lian Zhou Gao
Keyword(s):  
Particle Swarm Optimization ◽  
World Economy ◽  
Time Window ◽  
Time Windows ◽  
Particle Swarm ◽  
Modern Logistics ◽  
Swarm Optimization ◽  
Logistics Industry ◽  
Search Speed ◽  
Simulation Results

As the development of world economy, how to realize the reasonable vehicle logistics routing path problem with time window constrain is the key issue in promoting the prosperity and development of modern logistics industry. Through the research of vehicle logistics routing path 's demand, particle swarm optimization with a novel particle presentation is designed to solve the problem which is improved, effective and adept to the normal vehicle logistics routing. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability.

Precision Motion of Iterative Learning Controller Using Adaptive Filter Bandwidth Tuning by Improved Particle Swarm Optimization Technique

2013 ◽  
Vol 376 ◽  
pp. 349-353
Author(s):  
Yi Cheng Huang ◽  
Shu Ting Li ◽  
Kuan Heng Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Iterative Learning ◽  
Swarm Optimization ◽  
Filter Bandwidth ◽  
Improved Particle Swarm Optimization ◽  
Simulation Results ◽  
Adaptive Bandwidth ◽  
Precision Motion ◽  
Zero Phase

This paper utilized the Improved Particle Swarm Optimization (IPSO) technique for adjusting the gains of PID and the bandwidth of zero-phase Butterworth Filter of an Iterative Learning Controller (ILC) for precision motion. Simulation results show that IPSO-ILC-PID controller without adaptive bandwidth filter tuning have the chance of producing high frequencies in the error signals when the filter bandwidth is fixed for every repetition. However the learnable and unlearnable error signals should be separated for bettering control process. Thus the adaptive bandwidth of a zero phase filter in ILC-PID controller with IPSO tuning is applied to one single motion axis of a CNC table machine. Simulation results show that the developed controller can cancel the errors efficiently as repetition goes. The frequency response of the error signals is analyzed by the empirical mode decomposition (EMD) and the Hilbert-Huang Transform (HHT) method. Errors are reduced and validated by ILC with adaptive bandwidth filtering design.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals Short-term power load forecasting based on support vector machine and particle swarm optimization

2018 ◽  
Vol 13 ◽  
pp. 174830181879706 ◽  
Author(s):  
Song Qiang ◽  
Yang Pu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Influencing Factors ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Model Parameters ◽  
Short Term ◽  
Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

scholarly journals Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms

2018 ◽  
Author(s):  
Hsu-Tan Tan ◽  
Bor-An Chen ◽  
Yung-Fa Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Communication Systems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
System Capacity ◽  
Swarm Optimization ◽  
Device To Device ◽  
Simulation Results ◽  
Capacity Performance

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

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