Research of standard handwritten English letters recognition system based on the PSO-BP neural network

2011 ◽  
Author(s):  
Peng Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition System

scholarly journals Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Heng Ren ◽  
Yongjian Zhu ◽  
Ping Wang ◽  
Peng Li ◽  
Yuqun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Investigation ◽  
Recognition System ◽  
Quantitative Prediction ◽  
Widespread Application ◽  
Coal Roadway ◽  
Roof Stability ◽  
Stability Classification ◽  
The Stability

In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

Study on the Shift Schedule Based on Driver Type of Automatic Mechanical Transmission

2013 ◽  
Vol 734-737 ◽  
pp. 2721-2724
Author(s):  
Peng Han ◽  
Xiu Sheng Cheng ◽  
Yin Shu Wang ◽  
Xi Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition System ◽  
Mechanical Transmission ◽  
Neural Network Classifier ◽  
Driving Style ◽  
Different Types ◽  
Shift Schedules ◽  
Adaptive Ability ◽  
Intelligent Recognition

An intelligent recognition system of driver type suitable for different drivers was studied in this paper,and the driving style recognition based on BP neural network classifier structure was designed to make different types of shift schedules to adapt to different drivers.The intelligent recognition of driver type was verified by simulation.The rusults showed that the intelligent recognition based on BP neural network classifier structure had good adaptive ability,which could meet the requirements of different types of drivers.

Design of Sound Recognition System Based on Modified Neural Network

2013 ◽  
Vol 278-280 ◽  
pp. 1178-1181 ◽  
Author(s):  
Su Xiang Qian ◽  
Fu Xi Liu ◽  
Jian Cao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Threshold Value ◽  
Recognition System ◽  
Sound Signal ◽  
Sound Recognition ◽  
Characteristic Quantity ◽  
Lower Error

Sound recognition based on neural network is a technique that can put a resolution to exceeding artificial identification. Three kinds of neural network recognition models, adopting MFCC and difference MFCC, are discussed. According to six kinds of typical gunshots we design a kind of sound recognition system based on BP neural network optimized by PSO that uses MFCC and difference MFCC as a characteristic quantity to recognize sound signal. In the experiment PSO is used to optimize the network’s initial weights and threshold value. The experiment’s results show that BP neural network optimized by PSO using both MFCC characteristic quantity and difference MFCC characteristic quantity have a relatively lower error and a relatively faster speed than other ways discussed in the article, and the designed system reaches the expected goal.

Design and Realization of the SMT Product Character Recognition System

2010 ◽  
Vol 139-141 ◽  
pp. 1736-1739
Author(s):  
Hui Huang Zhao ◽  
De Jian Zhou ◽  
Zhao Hua Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Median Filter ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Network Module ◽  
Pass Filter ◽  
Low Pass Filter

We present an approach to recognizing characters in surface mount technology (SMT) product. An improved SMT product character recognition method is proposed which can obtain a good recognition rate. Some appropriate image processing algorithms, such as Gray processing, Low-pass Filter, Median Filter, and so on, are used to eliminate the noise. Then, Character image is obtained after character segmentation and character normalization. Finally, a three-layer back propagation (BP) neural network module is constructed. In order to improve the convergence rate of the network and avoid oscillation and divergence, the BP algorithm with momentum item is used. As a result, the SMT product character recognition system is developed. Experimental results indicate that the proposed character recognition can obtain satisfactory character-recognition rate and the recognition rate reached over by 98.6% when the hidden layer of BP neural network module has 20 nodes.

Research on Path Recognition System of Intelligent Vehicle Based on Neural Network

2013 ◽  
Vol 760-762 ◽  
pp. 1501-1504
Author(s):  
He Pan ◽  
Tai Hao Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Network Structure ◽  
Bp Neural Network ◽  
Recognition System ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Automatic Identification ◽  
Analog Simulation ◽  
Road Type

This paper studies on the path recognition system of intelligent vehicles, and makes classification and feature extraction of road. By using the BP neural network it makes the analog simulation, and through the training of network structure, it tries to establish network structure which can reflect the input road sample and the specified road type mappings, and then to realize the automatic identification of road type.

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