Study on Improved Flexible Neural Tree Optimization Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 1055-1059
Author(s):  
Yu Wang
Keyword(s):  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Structure Optimization ◽  
Programming Algorithm ◽  
Particle Swarm Algorithm ◽  
Tree Model ◽  
Local Minimum Point ◽  
Tree Optimization ◽  
Multi Expression Programming

The BP neural network is easy to fall into local minimum point, the algorithm convergence speed slow, this paper puts forward an improved algorithm of flexible neural tree, introduced the basic theory knowledge of Flexible neural tree , analyzes the characteristics and advantages of the neural tree. The structure optimization and parameter optimization are adopted some optimization algorithm, Introduced the multi expression programming algorithm for optimization of flexible neural tree structure and by using the improved particle swarm algorithm to optimize the parameters of flexible neural tree, Finally the establishment of complete flexible neural tree model.

The Application of DPSO - BP Neural Network in GIS Partial Discharge Typical Defects Recognition

2014 ◽  
Vol 889-890 ◽  
pp. 1078-1084 ◽  
Author(s):  
Ze Kun Liu ◽  
Hong Yu ◽  
Tie Qiao Guo ◽  
Cheng Long Xu ◽  
Zhi Wan Cheng
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Partial Discharge ◽  
Bp Algorithm ◽  
Bp Network ◽  
Network Training ◽  
Local Minimum Point ◽  
Learning Speed

The BP neural network is a classifier commonly used in partial discharge type recognition, but the traditional BP algorithm with defects cannot satisfy the actual need. So the optimization algorithm of BP network was studied intensively. DPSO algorithm was used for optimizing the network, and DPSO-BP algorithm is applied to analyze typical defects of GIS, which can be identified by the types of partial discharge. Compared with traditional BP algorithm, DPSO-BP algorithm occupied obvious advantage in recognition effect. It has improved the learning speed of the algorithm, effectively avoid network training going into local minimum point, and maintain the generalization ability and fault tolerance of BP neural network at the same time.

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.

Bp Neural Network Optimized by PSO and its Application in Function Approximation

2014 ◽  
Vol 945-949 ◽  
pp. 2413-2416 ◽  
Author(s):  
Jun Yi Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Function Approximation ◽  
Local Minimum ◽  
Simple Structure ◽  
Distributed Storage ◽  
Convergence Time ◽  
Bp Network ◽  
Network Training ◽  
Local Minimum Point

BP network is one of the most popular artificial neural networks because of its special advantage such as simple structure, distributed storage, parallel processing, high fault-tolerance performance, etc. However, with its extensive use in recent years, it is discovered that BP algorithm has the defects on slow convergent speed and easy convergence to a local minimum point. The paper proposes a method of BP Neural Network improved by Particle Swarm Optimization (PSO). The hybrid algorithm can not only avoid local minimum, but also raise the speed of network training and reduce the convergence time.

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

The Structure Optimization Design for the W-Type Metallic Sealing Ring Based on the MOGA Algorithm

2015 ◽  
Vol 727-728 ◽  
pp. 541-545
Author(s):  
Xiang Yu Ding
Keyword(s):  
Mechanical Properties ◽  
Optimal Design ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Structure Optimization ◽  
Ansys Software ◽  
Sealing Ring ◽  
Structure Optimization Design

This paper uses the ANSYS software to analysis the outer arm opening angel(OAOA) and the outer arm arc angle(OAAA) of W-type metallic sealing ring which can effects on the mechanical properties,obtained in that when the OAOA choose from 1.398°to 14.156 °and the OAAA choose from 30.21° to 59.5 °, the mechanical properties of the sealing ring can satisfy the requirement of use. Then using the MOGA optimization algorithm to optimize the design of W-type metallic sealing ring, and find when the OAOA choose 3.39°and the OAAA choose 32.18°are the optimal design of the W-type metallic sealing ring.

scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

The Prediction of the Error Rate in the Data Transmission Based on Improved Chaotic Optimization Neural Network

2013 ◽  
Vol 380-384 ◽  
pp. 1829-1833
Author(s):  
Xin Ping Liu ◽  
Jun Peng Xu ◽  
Hui Liu ◽  
Xiao Ling Wu
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Bp Neural Network ◽  
Data Transmission ◽  
Local Minimum ◽  
Continuous Wave ◽  
Transmission Error ◽  
Small Samples ◽  
Chaos Optimization ◽  
Neural Network Algorithm

As the slurry continuous wave changes according to the measurement of drilling (MWD) date, the precision of error rate prediction is low and the process of transferring data will be affected by signals. Based on the BP neural networks extensive mapping ability and chaos optimization algorithms global convergent ability, we structure a kind of improved chaos optimization of BP neural network algorithm. This algorithm can avoid several problems, such as the convergent speed of BP neural network is slow and the BP neural network is easy to sink into local minimum. With the powerful ability of generalization and prediction, this kind of algorithm can also be used to predict the data transmission error rate in slurry continuous wave. Under the condition of small samples, we create a model of data transmission in slurry continuous wave, which is based on improved chaos optimization of BP neural network. Simulate experiment has tested this algorithms feasibility and effectiveness

Sign in / Sign up

Export Citation Format

Share Document