Road Target Classification Method Based on GA-BP Neural Network for Event Camera

2021 ◽  
Author(s):  
Xuning Mao ◽  
Haiqing Liu ◽  
Miaomiao Zhang ◽  
Guang Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Classification Method ◽  
Target Classification ◽  
Event Camera

A Study on the Application of Learning Vector Quantization Neural Network in Pattern Classification

2014 ◽  
Vol 525 ◽  
pp. 657-660 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Vector Quantization ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Learning Vector Quantization ◽  
Classification Method ◽  
Two Dimensional ◽  
The One

Standard back propagation (BP) neural network has disadvantages such as slow convergence speed, local minimum and difficulty in definition of network structure. In this paper, an learning vector quantization (LVQ) neural network classifier is established, then it is applied in pattern classification of two-dimensional vectors on a plane. To test its classification ability, the classification results of LVQ neural network and BP neural network are compared with each other. The simulation result shows that compared with classification method based on BP neural network, the one based on LVQ neural network has a shorter learning time. Besides, its requirements for learning samples and the number of competing layers are also lower. Therefore it is an effective classification method which is powerful in classification of two-dimensional vectors on a plane.

Research on image classification method of strip steel surface defects based on improved Bat algorithm optimized BP neural network

2021 ◽  
pp. 1-13
Author(s):  
Xiaofeng Yue ◽  
Guoyuan Ma ◽  
Fuqiuxuan Liu ◽  
Xueliang Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Surface Defects ◽  
Fitness Function ◽  
Bat Algorithm ◽  
Classification Method ◽  
Bp Network ◽  
Strip Steel ◽  
Feature Values ◽  
Sensitivity Specificity

Due to the complexity and variety of textures on Strip steel, it is very difficult to detect defects on rigid surfaces. This paper proposes a metal surface defect classification method based on an improved bat algorithm to optimize BP neural network. First, this paper uses the Local Binary Pattern(LBP) algorithm to extract features from six types of defect images including inclusion, patches, crazing, pitted, rolled-in, and scratches, and build a feature sample library with the extracted feature values. Then, the WG-BA-BP network is used to classify the defect images with different characteristics. The weighted experience factor added by the network can control the flight speed of the bat according to the number of iterations and the change of the fitness function. And the gamma distribution is added in the process of calculating loudness, which enhances the local searchability. The BP network optimized by this method has higher accuracy. Finally, to verify the effectiveness of the method, this article introduces the five evaluation indicators of accuracy, precision, sensitivity, specificity, and F1 value under the multi-class model. To prove that this algorithm is more feasible and effective compared with other swarm intelligence algorithms. The best prediction performance of WG-BA-BP is 0.010905, and the accuracy rate can reach 0.9737.

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