scholarly journals Recognition of Rice Sheath Blight Based on a Backpropagation Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2907
Author(s):  
Yi Lu ◽  
Zhiyang Li ◽  
Xiangqiang Zhao ◽  
Shuaishuai Lv ◽  
Xingxing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Texture Features ◽  
Histogram Equalization ◽  
Recognition Algorithm ◽  
Sheath Blight ◽  
Background Information ◽  
Rice Sheath Blight ◽  
Hidden Layer ◽  
Color And Texture Features

Rice sheath blight is one of the main diseases in rice production. The traditional detection method, which needs manual recognition, is usually inefficient and slow. In this study, a recognition method for identifying rice sheath blight based on a backpropagation (BP) neural network is posed. Firstly, the sample image is smoothed by median filtering and histogram equalization, and the edge of the lesion is segmented using a Sobel operator, which largely reduces the background information and significantly improves the image quality. Then, the corresponding feature parameters of the image are extracted based on color and texture features. Finally, a BP neural network is built for training and testing with excellent tunability and easy optimization. The results demonstrate that when the number of hidden layer nodes is set to 90, the recognition accuracy of the BP neural network can reach up to 85.8%. Based on the color and texture features of the rice sheath blight image, the recognition algorithm constructed with a BP neural network has high accuracy and can effectively make up for the deficiency of manual recognition.

scholarly journals Music emotion recognition algorithm based on BP neural network

2022 ◽  
Vol 355 ◽  
pp. 03021
Author(s):  
Xu Liu ◽  
Pingxiao Ge
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Bp Neural Network ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Expected Effect ◽  
Hidden Layer

Music plays a very important role in animation production. Because it could better express the emotion of the character, this paper uses BP neural network to identify the music emotion. This paper first introduced the structure of BP neural network. Then, the parameters and structure of the network were designed according to the category of music emotion. Finally, a three-layer BP neural network with 5 input nodes, 13 hidden layer nodes and 4 output nodes was constructed and applied to music emotion recognition. The recognition accuracy was 85.02%, which basically met the requirements of music emotion recognition and achieves the expected effect.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 891-897
Author(s):  
G.Q. Shang ◽  
C.H. Sun ◽  
X.F. Chen ◽  
J.H. Du
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fused Deposition Modeling ◽  
Rapid Prototype ◽  
Fused Deposition ◽  
Novel Approach ◽  
Deposition Modeling ◽  
Hidden Layer ◽  
Parts Manufacturing ◽  
Bp Neural Network Model

Fused deposition modeling (FDM) has been widely applied in complex parts manufacturing and rapid tooling and so on. The precision of prototype was affected by many factors during FDM, so it is difficult to depict the process using a precise mathematical model. A novel approach for establishing a BP neural network model to predict FDM prototype precision was proposed in this paper. Firstly, based on analyzing effect of each factor on prototyping precision, some key parameters were confirmed to be feature parameters of BP neural networks. Then, the dimensional numbers of input layer and middle hidden layer were confirmed according to practical conditions, and therefore the model structure was fixed. Finally, the structure was trained by a great lot of experimental data, a model of BP neural network to predict precision of FDM prototype was constituted. The results show that the error can be controlled within 10%, which possesses excellent capability of predicting precision.

Prediction Model of Flow-Induced Noise in Large-Scale Centrifugal Pumps Based on BP Neural Network

2017 ◽  
Author(s):  
Chang Guo ◽  
Ming Gao ◽  
Peixin Dong ◽  
Yuetao Shi ◽  
Fengzhong Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Centrifugal Pumps ◽  
Output Layer ◽  
Sample Data ◽  
Input Layer ◽  
Flow Induced Noise ◽  
Hidden Layer

As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.

Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence

2019 ◽  
Vol 34 (06) ◽  
pp. 2054016
Author(s):  
Shuyu Hu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Data Acquisition ◽  
Bp Neural Network ◽  
Decisive Role ◽  
Recognition Algorithm ◽  
Important Research Topic ◽  
Information Recognition ◽  
Handwritten Signature ◽  
Interference Background

At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms.

scholarly journals Development of Customer Relationship Management System in E-Commerce Based on BP Neural Network

2012 ◽  
Vol 6-7 ◽  
pp. 995-999
Author(s):  
Mei Ling Zhou ◽  
Jing Jing Hao
Keyword(s):  
Neural Network ◽  
Customer Relationship Management ◽  
Bp Neural Network ◽  
Management System ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Feed Forward Network ◽  
Neural Network Algorithm ◽  
Hidden Layer

BP neural network can learn and store a lot of input - output mode mapping, without prior reveal the mathematical equations describe the mapping. The model based on BP neural network algorithm is constituted by an input layer, output layer and one hidden layer, three-layer feed forward network. CRM is to acquire, maintain and increase the methods and processes of profitable customers. The core of CRM is the customer value management, customer value; it is divided into the de facto value, potential value and model value. The paper presents development of customer relationship management system in e-commerce based on BP neural network. The experiment shows BP is superior to RFCA in CRM.

The Use of BP Neural Network in Evaluation about the Appearance of the Garments

2012 ◽  
Vol 433-440 ◽  
pp. 4320-4323 ◽  
Author(s):  
Jing Wang ◽  
Jin Ying Song ◽  
Ai Qing Tang
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Original Data ◽  
Back Propagation Algorithm ◽  
Bp Network ◽  
Self Adaptation ◽  
Hidden Layer ◽  
Good Agreement

This article reports the use of BP neural network for evaluation of the appearance of garment after dry wash. The selected data about parameters of fabrics and interlinings are analyzed by principal analysis and eight principal components are obtained through this method. A BP neural network with a single hidden layer is constructed including eight input nodes, six hidden nodes and one output nodes. During training the network with a back-propagation algorithm, the eight principal components are used as input parameters, while the rate of the appearance of the garment are used as output parameters. The weight values are modified with momentum and learning rate self-adaptation to solve the two defects of the BP network. All original data are preprocessed and the learning process is successful in achieving a global error minimum. The rate of the appearance can be evaluated with this training network and there is a good agreement between the evaluated and tested values.

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