scholarly journals Supervised evolutionary learning: Use of gradient histogram & particle swarm algorithm to detection & tracking pedestrian in sequence of infrared images

2021 ◽  
Vol 9 (1) ◽  
pp. 1-17
Author(s):  
Karim Zare ◽  
Seyedmohammad Shahrokhi ◽  
Mohammadreza Amini
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Pedestrian Detection ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Evolutionary Learning ◽  
Infrared Images ◽  
Learning Capability ◽  
Learner Model ◽  
New Processing

Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification.  In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response.  There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers.  Researchers are looking for new processing models that can accurately monitor one's position on the move.  In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented.  It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images.  As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning.  The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms.

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhigang Wang ◽  
Aijun Li ◽  
Lihao Wang ◽  
Xiangchen Zhou ◽  
Boning Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Aerodynamic Parameter ◽  
Content Type ◽  
Flight Data ◽  
Output Error ◽  
Aerodynamic Derivatives ◽  
Levenberg Marquardt ◽  
Error Method

Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

scholarly journals Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luxin Jiang ◽  
Xiaohui Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Teaching Quality ◽  
Particle Swarm Algorithm ◽  
Bp Model ◽  
Quality Evaluation Model

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

Fault Diagnosis of Engine Ignition System Based on the Optimized BP Neural Networks by Improved Particle Swarm Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 366-370
Author(s):  
Zhi Mei Duan ◽  
Xiao Jin Yuan ◽  
Yan Jie Zhou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Particle Swarm Algorithm ◽  
Classification Result ◽  
Bp Network ◽  
Ignition System ◽  
Good Classification

In order to improve the accuracy of fault diagnosis of engine ignition system, in this paper, adaptive mutation particle swarm optimization (AMPSO) algorithm is used to optimize the weight of BP neural network. According to the fault feature of engine ignition system, the fault diagnosis is accomplished by the optimized BP neural network. The algorithm overcomes disadvantages that slowly convergence and easy to fall into local minima of standard PSO and BP network. The simulation results show that the method gains good classification result and has a certain practicality.

Application of Particle Swarm Algorithm in Train-Set Circulation Problem of China

2012 ◽  
Vol 253-255 ◽  
pp. 1235-1240
Author(s):  
Hua Li ◽  
Bao Ming Han ◽  
Fang Lu ◽  
Xiao Juan Li
Keyword(s):  
High Speed ◽  
Particle Swarm Optimization Algorithm ◽  
Programming Model ◽  
Real Life ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
High Speed Railway ◽  
Railway Line ◽  
Circulation Problem

Train-set circulation problem is an important issue in operations of high-speed passenger trains in the world. On the basis of characteristics of the train-set circulation problem in China, an integer programming model is presented without considering distinct train-set types. With redefinitions of some basic mathematical objects and operations, an improved particle swarm optimization algorithm is proposed to solve the model. The algorithm is applied in a real-life case study based on the timetable of the Wuhan-Guangzhou High-speed Railway Line. The results show that the proposed algorithm is effective to find the optimized train-set circulation plan.

Tuning the Structure and Parameters of a Neural Network by Using Cooperative Quantum Particle Swarm Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 1328-1332 ◽  
Author(s):  
Qi Feng Tang ◽  
Liang Zhao ◽  
Rong Bin Qi ◽  
Hui Cheng ◽  
Feng Qian
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Training Algorithms ◽  
Approximation Accuracy ◽  
Quantum Genetic Algorithm ◽  
Cooperation Mechanism ◽  
Swarm Algorithm ◽  
Redundant Structure

In this paper, a cooperative quantum genetic algorithm-particle swarm algorithm (CQGAPSO) is applied to tune both structure and parameters of a feedforward neural network (NN) simultaneously. In CQGAPSO algorithm, QGA is used to optimize the network structure and PSO algorithm is employed to search the parameters space. The amplitude-based coding method and cooperation mechanism improve the learning efficiency, approximation accuracy and generalization of NN. Furthermore, the ill effects of approximation ability caused by redundant structure of NN are eliminated by CQGAPSO. The experimental results show that the proposed method has better prediction accuracy and robustness in forecasting the sunspot numbers problems than other training algorithms in the literatures.

scholarly journals Feature Selection Based on BP Neural Network and Adaptive Particle Swarm Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bingsheng Chen ◽  
Huijie Chen ◽  
Mengshan Li
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Data Sets ◽  
Swarm Optimization

Feature selection can classify the data with irrelevant features and improve the accuracy of data classification in pattern classification. At present, back propagation (BP) neural network and particle swarm optimization algorithm can be well combined with feature selection. On this basis, this paper adds interference factors to BP neural network and particle swarm optimization algorithm to improve the accuracy and practicability of feature selection. This paper summarizes the basic methods and requirements for feature selection and combines the benefits of global optimization with the feedback mechanism of BP neural networks to feature based on backpropagation and particle swarm optimization (BP-PSO). Firstly, a chaotic model is introduced to increase the diversity of particles in the initial process of particle swarm optimization, and an adaptive factor is introduced to enhance the global search ability of the algorithm. Then, the number of features is optimized to reduce the number of features on the basis of ensuring the accuracy of feature selection. Finally, different data sets are introduced to test the accuracy of feature selection, and the evaluation mechanisms of encapsulation mode and filtering mode are used to verify the practicability of the model. The results show that the average accuracy of BP-PSO is 8.65% higher than the suboptimal NDFs model in different data sets, and the performance of BP-PSO is 2.31% to 18.62% higher than the benchmark method in all data sets. It shows that BP-PSO can select more distinguishing feature subsets, which verifies the accuracy and practicability of this model.

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