An Unmanned Helicopter Model Identification Method Based on the Immune Particle Swarm Optimization Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3890-3893 ◽  
Author(s):  
Ting Ting Yang ◽  
Ai Jun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Model ◽  
Model Identification ◽  
Particle Swarm ◽  
Flight Test ◽  
Pso Algorithm ◽  
Model Parameters ◽  
Unmanned Helicopter ◽  
Swarm Optimization ◽  
Identification Method

An unmanned helicopter dynamic model identification method based on immune particle swarm optimization (PSO) algorithm is approved in this paper. In order to improve the search efficiency of PSO and avoid the premature convergence, the PSO algorithm is combined with the immune algorithm. The unmanned helicopter model parameters are coded as particle, the error of flight test and math simulation model is objective function, and the dynamic model of unmanned helicopter is identified. The simulation result shows that the method has high identification precision and can realistically reflect the dynamic characteristics.

Particle swarm optimization for simultaneous analysis of magnetotelluric and time-domain electromagnetic data

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. E151-E159 ◽  
Author(s):  
Alessandro Santilano ◽  
Alberto Godio ◽  
Adele Manzella
Keyword(s):  
Particle Swarm Optimization ◽  
Time Domain ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Simultaneous Analysis ◽  
Simultaneous Optimization ◽  
Joint Analysis ◽  
Model Parameters ◽  
Static Shift ◽  
Swarm Optimization

We have developed an innovative, simultaneous 1D optimization of electromagnetic (EM) data. Our scheme is suitable for the simultaneous analysis of magnetotelluric (MT) and time-domain EM (TDEM) data based on the probabilistic and evolutionary particle swarm optimization (PSO) algorithm. The simultaneous optimization also identifies and removes the static shift from the MT data. In our scheme, the static shift of the MT apparent resistivity curve is considered as an additional parameter [Formula: see text] to be optimized. We tested the suggested method on the synthetic data and then applied it to the data from an EM geophysical study carried out in the geothermal area of Larderello-Travale (Tuscany, Italy). Apart from the novelty of using the PSO algorithm to estimate the model parameters by joint analysis, the simultaneous optimization of the static shift parameter addresses a major problem in MT, i.e., how to define and remove the galvanic effects on MT curves according to independent information, such as that provided by TDEM data. The procedure is expected to strongly influence the application of MT, particularly in geothermal exploration, which commonly relies extensively on EM methods.

scholarly journals Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage using Modified Particle Swarm Optimization

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 315
Author(s):  
Khubab Ahmed ◽  
Peng Yan ◽  
Su Li
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Model Identification ◽  
Particle Swarm ◽  
Identification Problem ◽  
Search Space ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Local Optima ◽  
Modified Particle Swarm Optimization

This paper presents modeling and parameter identification of the Duhem model to describe the hysteresis in the Piezoelectric actuated nano-stage. First, the parameter identification problem of the Duhem model is modeled into an optimization problem. A modified particle swarm optimization (MPSO) technique, which escapes the problem of local optima in a traditional PSO algorithm, is proposed to identify the parameters of the Duhem model. In particular, a randomness operator is introduced in the optimization process which acts separately on each dimension of the search space, thus improving convergence and model identification properties of PSO. The effectiveness of the proposed MPSO method was demonstrated using different benchmark functions. The proposed MPSO-based identification scheme was used to identify the Duhem model parameters; then, the results were validated using experimental data. The results show that the proposed MPSO method is more effective in optimizing the complex benchmark functions as well as the real-world model identification problems compared to conventional PSO and genetic algorithm (GA).

Fitting of adaptive neuron model to electrophysiological recordings using particle swarm optimization algorithm

2017 ◽  
Vol 31 (05) ◽  
pp. 1750023 ◽  
Author(s):  
Bonan Shan ◽  
Jiang Wang ◽  
Lvxia Zhang ◽  
Bin Deng ◽  
Xile Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Firing Rate ◽  
Neuron Model ◽  
Estimation Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Electrophysiological Recordings ◽  
Spiking Neuron Model

In order to fit neural model’s spiking features to electrophysiological recordings, in this paper, a fitting framework based on particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters in an augmented multi-timescale adaptive threshold (AugMAT) model. PSO algorithm is an advanced evolutionary calculation method based on iteration. Selecting a reasonable criterion function will ensure the effectiveness of PSO algorithm. In this work, firing rate information is used as the main spiking feature and the estimation error of firing rate is selected as the criterion for fitting. A series of simulations are presented to verify the performance of the framework. The first step is model validation; an artificial training data is introduced to test the fitting procedure. Then we talk about the suitable PSO parameters, which exhibit adequate compromise between speediness and accuracy. Lastly, this framework is used to fit the electrophysiological recordings, after three adjustment steps, the features of experimental data are translated into realistic spiking neuron model.

scholarly journals Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Michala Jakubcová ◽  
Petr Máca ◽  
Pavel Pech
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Problems ◽  
Hydrological Model ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Model Parameters ◽  
Estimation Of Parameters ◽  
Swarm Optimization

The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO) algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.

scholarly journals Geographical Location Estimation based on An Improved Particle Swarm Optimization

2016 ◽  
Vol 8 (4) ◽  
pp. 20
Author(s):  
Zhuojie Chen ◽  
Lang Deng ◽  
Yuanbiao Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Geographical Location ◽  
Pso Algorithm ◽  
Location Estimation ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Local Optima ◽  
Precise Tool ◽  
Improved Pso

This study presents a strategy for determining the relatively accurate geographical location of an object based on a video or a sequence of images taken at regular intervals. On the basis of a series of solar formulas, a precise object’s shadow length model that includes the latitude and longitude of the photographer is built to describe how the shadow changes. Inputting the shadow length of the object and the creation time of the image into the built model, parameters like the latitude or longitude can be estimated by Particle Swarm Optimization (PSO). To solve the problem that PSO is easy to get stuck into local optima, a compression factor and mutation operation are introduced to the algorithm. Through analyzing the instance, the improved PSO algorithm has demonstrated itself with enhancement in convergence and accuracy. In conclusion, the improved PSO is an effective and precise tool to estimate the geographical location of video or images.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

Sign in / Sign up

Export Citation Format

Share Document