scholarly journals Motion Reliability Analysis of Unlocking Trigger Device Based on CPSO-BR-BP Neural Network with Uncertain Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yun Tian ◽  
Hongtao Fan ◽  
Yuliang Zhang ◽  
Licheng Liu ◽  
Kang Gong
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Particle Swarm ◽  
Separation Process ◽  
Structural Function ◽  
Particle Swarm Algorithm ◽  
Computationally Efficient ◽  
Proxy Model

Aiming at overcoming the problem that the mechanism function of the unlocking trigger device is difficult to obtain and the corresponding reliability analysis cannot be performed, a motion reliability analysis method based on the CPSO-BR-BP neural network proxy model is proposed. Firstly, the particle swarm algorithm is optimized through the chaotic sequence, and the back-propagation (BP) neural network is optimized using Chaos Particle Swarm Optimization (CPSO) and Bayesian Regularization (BR) algorithm. The CPSO-BR-BP neural network proxy model is established, and the reliability of shape memory alloys (SMA) wire unlocking based on the structural function is calculated. Moreover, according to the structural function of the separation process, the motion reliability based on the proxy model and the improved membership function is calculated. Finally, a series reliability model is established based on the unlocking process and the separation process to calculate the reliability of the whole machine. The reliability of the unlocking trigger device is analyzed by the proposed method. Results show that the proposed method is computationally efficient with the calculated reliability of 0.9987.

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.

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.

scholarly journals State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network

Batteries ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 69 ◽  
Author(s):  
Chuan-Wei Zhang ◽  
Shang-Rui Chen ◽  
Huai-Bin Gao ◽  
Ke-Jun Xu ◽  
Meng-Yue Yang
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Training Data ◽  
Particle Swarm Algorithm ◽  
Power Battery

Accurately estimating the state of charge (SOC) of power batteries in electric vehicles is of great significance to the measurement of the endurance mileage of electric vehicles, as well as the safety protection of the power battery. In view of lithium ion batteries’ nonlinear relation between SOC estimation and current, voltage, and temperature, the improved Back Propagation (BP) neural network method is proposed to accurately estimate the SOC of power batteries. To address the inherent limitations of BP neural network, particle swarm algorithm is adopted to modify the relevant weighting coefficients. In this paper, the lithium iron phosphate battery (3.2 V/20 Amper-Hour) was studied. Charge and discharge experiments were conducted under a constant temperature. The training data were used to construct the surrogate model using the improved BP neural network. It is noted that the accuracy of the developed algorithm is increased by 2% as compared to that of conventional BP. Finally, an actual vehicle condition experiment was designed to further verify the accuracy of these two algorithms. The experimental results show that the improved algorithm is more suitable for real vehicle operating conditions than the traditional algorithm, and the estimation accuracy can meet the industry standards to a greater extent.

Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on Particle Swarm Neural Network

2013 ◽  
Vol 712-715 ◽  
pp. 1965-1969 ◽  
Author(s):  
Bao Ru Han ◽  
Jing Bing Li ◽  
Heng Yu Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Analog Circuit ◽  
Fitness Function ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Soft Fault ◽  
Soft Fault Diagnosis

This paper presents a tolerance analog circuit hard fault and soft fault diagnosis method based on the BP neural network and particle swarm optimization algorithm. First, select the mean square error function of BP neural network as the fitness function of the PSO algorithm. Second, change the guidance of neural network algorithms rely on gradient information to adjust the network weights and threshold methods, through the use of the characteristics of the particle swarm algorithm groups parallel search to find more appropriate network weights and threshold. Then using the adaptive learning rate and momentum BP algorithm to train the BP neural network. Finally, the network is applied to fault diagnosis of analog circuit, can quickly and effectively to the circuit fault diagnosis.

Combined Forecasting Model Based on BP Improved by Particle Swarm Optimization and its Application

2014 ◽  
Vol 644-650 ◽  
pp. 1954-1956
Author(s):  
Run Ya Li ◽  
Xiang Nan Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Power Load ◽  
Bp Neural Network Model

The BP neural network as the traditional prediction method has certain advantages, but it has some drawbacks, Such as slow convergence and sensitive to the initial weights, etc. The PSO algorithm is introduced into the neural network training, using the particle swarm algorithm to optimize the neural network weights and threshold. Through the establishment of the particle swarm - BP neural network model for power load budget, it improves the accuracy and stability of the forecast.

Study on Recognition of Fertilized Chicken Eggs Based on Particle Swarm Optimized Neural Network

2013 ◽  
Vol 846-847 ◽  
pp. 659-662
Author(s):  
Jian Yang ◽  
Chun Yan Xia ◽  
Ying Shi ◽  
Ying Ying Yin ◽  
Xin Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Hatching Rate ◽  
Output Vector ◽  
Input And Output ◽  
Collection Device ◽  
Chicken Eggs ◽  
Image Collection

In order to detect fertilized chicken eggs nondestructively to improve hatching rate, this paper uses the method of image processing and optimizing BP neural network by particle swarm to identify fertilized chicken eggs. Firstly, we use image collection device to collect images of the unfertilized and fertilized chicken eggs, to extract the feature of egg image, and then determine the input and output vector, while optimized neural network by particle swarm is 5 dimensional input and 1 dimensional outputs. Finally, we use particle swarm algorithm to optimize the weights and threshold of neural network, which can be used to predict the condition of fertilization. The experiment shows that, compared with the traditional BP neural network, it is more accurate to recognize the fertilized chicken eggs when using optimized BP neural network by particle swarm. The rate can reach 98.21%, which meets the requirements of recognizing fertilized chicken eggs.

Image Recognition Based on Chaotic-Particle Swarm-Optimization-Neural Network Algorithm

2013 ◽  
Vol 655-657 ◽  
pp. 969-973
Author(s):  
Bo Li ◽  
Song Xin Shi ◽  
Shi Wang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Image Recognition ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Chaotic Particle Swarm Optimization

A novel image recognition method based on chaotic-particle swarm-optimization-neural network algorithm was presented. The chaotic mapping mechanism and particle swarm algorithm were used to optimize the weight and threshold of BP neural network which was applied to the recognition of image. The simulation results show this new method can overcome the problems that BP neural network is easy to fall into local optimum and sensitive to the initial value, and has better recognition rate and stronger robustness.

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