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.

scholarly journals Study of BP neural network based on improved particle swarm algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032041
Author(s):  
Xiaoqian Ma ◽  
Liyuan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Particle Swarm ◽  
Correction Method ◽  
Series Data ◽  
Particle Swarm Algorithm ◽  
The Neural Network ◽  
Swarm Algorithm

Abstract This paper uses first-order difference to transform non-smooth data into smooth time series data, determines the p and q parameters in the model by judging the trailing and truncated nature of ACF, PACF, and finally establishes the ARIMA model after ACI, BCI detection. According to the parameters of the neural network randomly selected similar to the initial spatial position of the particles in the particle swarm algorithm, the improved particle swarm algorithm is used instead of the gradient correction method to precisely adjust the parameters and establish the BP neural network, which improves the robustness and accuracy of the prediction model.

Research on Particle Swarm Optimsiation and its Application in Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 5869-5872
Author(s):  
Yue Li Li ◽  
Shu Hui Chang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Generalization Ability ◽  
Learning Capacity ◽  
The Neural Network

This paper based on the PSO algorithm is a neural network model, and with other learning algorithm, and the results show that the performance comparisons are based on improved PSO algorithm two perceptron networks have higher classification accuracy and strong generalization ability. Particle Swarm Optimization (PSO) as an emerging evolutionary algorithm fast convergence speed, robustness, global search ability, and does not need the help of the characteristics of the problem itself (such as gradient). Combination of PSO and neural network PSO algorithm to optimize the connection weights of the neural network can be used to overcome the problem of BP neural network can not only play the generalization ability of the neural network, but also can improve the convergence rate of the neural network and learning capacity.

Integration Particle Swarm Algorithm and its Application in Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2133-2136
Author(s):  
Jun Pan ◽  
Xu Cao
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Algorithm Optimization ◽  
Bp Network ◽  
Node Number ◽  
The Neural Network ◽  
Swarm Algorithm ◽  
Hidden Layer

This paper puts forward a kind of evolutionary algorithm and the neural network combining with the new method of optimization of hidden layer nodes number of particle swarm algorithm of neural network. The BP neural network technology is a kind of more mature neural network method, but there are easy to fall into local minimum value, unable to accurately determine the number of hidden layer nodes of the network, the disadvantages such as slow convergence speed. This paper puts forward the optimization with hidden node number of particle swarm neural network (HPSO neural network) is the hidden layer of BP network node number as a particle swarm optimization (PSO) algorithm is an important optimization goal, network of hidden layer nodes and the number of each BP network weights and closed value together, common as particle swarm algorithm optimization goal.

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

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.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

Application of BP Neural Network Based on Immune Particle Swarm Optimization for Fault Diagnosis of Power Transformer

2013 ◽  
Vol 448-453 ◽  
pp. 3605-3609
Author(s):  
Yu Xin Zhang ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Network Training ◽  
Gradient Descent Algorithm ◽  
Ipso Algorithm

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

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.

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