scholarly journals Frequency Diverse Array Target Localization Based on IPSO-BP

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qinghua Liu ◽  
Kai Ding ◽  
Bingsen Wu ◽  
Quanmin Xie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Target Localization ◽  
Single Frequency ◽  
Localization Algorithms ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Frequency Diverse Array ◽  
Good Learning ◽  
Ipso Algorithm

For the traditional target localization algorithms of frequency diverse array (FDA), there are some problems such as angle and distance coupling in single-frequency receiving FDA mode, large amount of calculation, and weak adaptability. This paper introduces a good learning and predictive method of target localization by using BP neural network on FDA, and FDA-IPSO-BP neural network algorithm is formed. The improved particle swarm optimization (IPSO) algorithm with nonlinear weights is developed to optimize the neural network weights and biases to prevent BP neural network from easily falling into local minimum points. In addition, the decoupling of angle and distance with single frequency increment is well solved. The simulation experiments show that the proposed algorithm has better target localization effect and convergence speed, compared with FDA-BP and FDA-MUSIC algorithms.

Image Segmentation of Eggplants in Growing Environment Based on Improved BP Neural Network

2010 ◽  
Vol 40-41 ◽  
pp. 599-603
Author(s):  
Jian Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Bp Neural Network ◽  
Image Features ◽  
The Neural Network ◽  
Training Samples ◽  
Neural Network Algorithm ◽  
Growing Environment ◽  
Improved Bp Algorithm ◽  
Gray Values

Aim at the complex background of eggplant image in the growing environment, a image segmentation method based on BP neural network was put forward. The EXG gray values of 3×3 neighborhood pixels were obtained as image features through by analyzing the eggplant image. 30 eggplant images were taken as training samples and results of manual segmentation images by Photoshop were regarded as teacher signals. The improved BP algorithm was used to train the parameter of the neural network. The effective parameter was achieved after 120 times of training. The result of this experiment showed that the eggplant fruit could be preferably segmented from the background by using BP neural network algorithm and it could totally meet the demands of the picking robots after further processing by way of combining mathematics morphology with median filtering.

Research on the Application of the Prediction of the Expressway Traffic Flow Based on the Neural Network with Genetic Algorithm

2011 ◽  
Vol 189-193 ◽  
pp. 4400-4404 ◽  
Author(s):  
Chun Mei Zhu ◽  
Chang Peng Yan ◽  
Xiao Li Xu ◽  
Guo Xin Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Mixed Model ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Speed Up ◽  
Neural Network Algorithm ◽  
Genetic Neural Network

In order to improve the efficiency and accuracy of the prediction of expressway traffic flow, this paper, based on the characteristics of the data of the expressway traffic flow, focuses on an optimized method of prediction with the application of the neural network with genetic algorithm. Applying genetic algorithm, optimizing BP neural network structure and establishing a new mixed model, this algorithm speed up the slow convergence velocity of traditional BP neural network prediction and increases the possibility to escape local minima. This algorithm based on the optimized genetic neural network predicts the actual data of the expressway traffic flow, the result of which shows that the application of the optimized method of prediction with the genetic neural network algorithm is effective and that it improves the rate and the accuracy of the prediction of the expressway traffic flow.

Study on Data Security Technology of Neural Network System

2014 ◽  
Vol 701-702 ◽  
pp. 1041-1044
Author(s):  
Yan Wei Hong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Security Technology ◽  
Fusion Algorithm ◽  
Basic Principles ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Algorithm ◽  
And Training

This paper analyzes the neural network algorithm model, introduces the basic principles and training process of BP neural network algorithm, analyzes the BP neural network weights adjustment processand the method of determining the number of nodes in each layer; in improved protocol algorithm basis LEACH-E, combined with the BP neural network algorithm, we propose a new data fusion algorithm BPDFA to reduce energy consumption to attain the network lifetime goal.

Study on PCA-Based BP Neural Network Algorithm

2014 ◽  
Vol 530-531 ◽  
pp. 517-521
Author(s):  
Jian Qing Hong ◽  
De'an Zhao ◽  
Wei Kuan Jia
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Computing Time ◽  
Principal Component ◽  
Bp Algorithm ◽  
Complex Data ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Low Dimensional

Using the neural network to deal with complex data, because the pending sample with many variables, aiming at this nature of the pending sample and the structure properties of the BP neural network, in this paper, we propose the new BP neural network algorithm base on principal component analysis (PCA-BP algorithm). The new algorithm through PCA dimension reduction for complex data, got the low-dimensional data as the BP neural networks input, it will be beneficial to design the hidden layer of neural network, save a lot of storage space and computing time, and conductive to the convergence of the neural network. In order to verify the validity of the new algorithm, compared with the traditional BP algorithm, through the case analysis, the result show that the new algorithm improve the efficiency and recognition precise, worthy of further promotion.

Learning perception prediction and English hierarchical model based on neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Zhang Wenjuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Performance ◽  
Large Data ◽  
Complex Data ◽  
Examination System ◽  
Model Based ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

The traditional English examination and the current examination system have been unable to meet the needs of the education industry for English examinations. In view of this, based on the neural network algorithm, this study proposes a hierarchical network management model from the user’s perspective. Based on the in-depth study of the neural network, this study combined with the network performance characteristics of large data volume, complex data to propose a new BP neural network algorithm. By dynamically changing the momentum factor and learning rate, the algorithm has greatly improved the accuracy and stability of the error. In addition, this study proposes a user perception prediction model, and the model is continuously trained on the model based on the improved BP neural network algorithm and the monitored network performance. In order to study the performance of the research model, a control experiment is designed to analyze the performance of the model. The research results show that the intelligent model and algorithm proposed in this paper are completely feasible and effective.

scholarly journals Analysis of DES Plaintext Recovery Based on BP Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Sijie Fan ◽  
Yaqun Zhao
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Bp Neural Network ◽  
Layer By Layer ◽  
Network Simulator ◽  
Analysis Model ◽  
Backpropagation Neural Network ◽  
Network Algorithms ◽  
The Neural Network ◽  
Neural Network Algorithm

Backpropagation neural network algorithms are one of the most widely used algorithms in the current neural network algorithm. It uses the output error rate to estimate the error rate of the direct front layer of the output layer, so that we can get the error rate of each layer through the layer-by-layer backpropagation. The purpose of this paper is to simulate the decryption process of DES with backpropagation algorithm. By inputting a large number of plaintext and ciphertext pairs, a neural network simulator for the decryption of the target cipher is constructed, and the ciphertext given is decrypted. In this paper, how to modify the backpropagation neural network classifier and apply it to the process of building the regression analysis model is introduced in detail. The experimental results show that the final result of restoring plaintext of the neural network model built in this paper is ideal, and the fitting rate is higher than 90% compared with the true plaintext.

Research on the Automotive Suspension Based on BP Neural Network

2013 ◽  
Vol 341-342 ◽  
pp. 478-481
Author(s):  
Tai Hao Li ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Bp Neural Network ◽  
Fuzzy Neural Network ◽  
Air Suspension ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Intelligence Theory ◽  
Neural Network Algorithm ◽  
Automotive Suspension

This article uses the application of artificial intelligence theory to research on the air suspension system, constructing the structure of control system, and the study of the neural network algorithm is simulation for its study of results. The fusion of fuzzy logic and neural network consist of the fuzzy neural network, which has the advantages of fuzzy logic and neural network.

Classification of High Quality Carbon Steel Based on BP Neural Network

2013 ◽  
Vol 873 ◽  
pp. 54-59
Author(s):  
Lan Lan Liu ◽  
Tao Hong Zhang ◽  
Yong Hong Xie ◽  
Li Li ◽  
De Zheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Practical Application ◽  
High Quality ◽  
Standard Data ◽  
The Neural Network ◽  
Neural Network Algorithm

Now carbon steel is used in the engineering aspects and it is the oldest and the largest amount of basic materials. How to determine whether they are high-quality carbon steel? In this paper the standard data of high quality carbon steel by using the classical BP neural network algorithm is researched. Then it is simulated and predicted. The final comprehensive evaluation and analysis show that the neural network model can be used to decide whether it is a high quality carbon steel. Further, it has a good practical application value for utilizing high-quality carbon steel rationally.

scholarly journals Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guiting Ren
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Bp Network ◽  
English Curriculum ◽  
Bee Colony ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Solution Accuracy

The traditional BP neural network has the disadvantages of easy falling into local minimum and slow convergence speed. Aiming at the shortcomings of BP neural network (BP neural network), an artificial bee colony algorithm (ABC) is proposed to cross-optimize the weight and threshold of BP network parameters. This study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. It includes the application of BP neural network algorithm in English course recommendation technology, English course teaching design mode, the application of BP neural network algorithm in English course, and the optimal combination of bee colony algorithm and BP neural network. After 4690 iterations, the neural network reaches the target accuracy, and the training is completed. At the same time, the prediction error of the model is less than 10%, which further shows that the performance of the prediction model is good. Therefore, the combination model is recommended in this paper. The results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network.

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