scholarly journals PSO–SOM Neural Network Algorithm for Series Arc Fault Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Na Qu ◽  
Jiatong Chen ◽  
Jiankai Zuo ◽  
Jinhai Liu
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Pso Algorithm ◽  
Self Organization ◽  
Detection Accuracy ◽  
Competitive Neural Network ◽  
Neural Network Algorithm ◽  
Som Network ◽  
Som Neural Network ◽  
Self Learning

Self-organizing feature map (SOM) neural network is a kind of competitive neural network with unsupervised learning. It has the strong abilities of self-organization and self-learning. However, the classification accuracy of SOM neural network may decrease when the features of tested object are not obvious. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the weight values of SOM network. Three indexes, i.e., intra-class density, standard deviation and sample difference, are used to judge the weight value, which can improve the classification accuracy of the SOM network. PSO–SOM network is applied to the detection of series arc fault in electrical circuits and compared with conventional SOM network and learning vector quantization (LVQ) network. The detection accuracy of the PSO–SOM network is 95%, which is higher than conventional SOM network and LVQ network.

Research on the Text Classification Method with Self-Organization Network Model

2011 ◽  
Vol 179-180 ◽  
pp. 940-944
Author(s):  
Jian Chen ◽  
Wen Rong Jiang ◽  
Ji Hong Yan
Keyword(s):  
Neural Network ◽  
National Security ◽  
Network Model ◽  
Text Classification ◽  
Classification Method ◽  
Self Organization ◽  
Crucial Issue ◽  
Text Understanding ◽  
Som Neural Network ◽  
Massive Information

Information retrieval is crucial issue for many areas, like industry, national security, disease control, etc. and how to organize massive information into understandable, readable automatically is a key step to understand data or information. Text Classification is a key issue for automatically text understanding, this paper provides a text classification method based on the SOM neural network model and delivers a reasonable performance.

The Analysis of Faults Detection Software Based on Improved Neural Network Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2044-2047
Author(s):  
Miao Yan ◽  
Zhi Bao Liu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Improved Method ◽  
Software Faults ◽  
Component Software ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Detection Software

The large-scale software is consisted of the components which are quite different. The detection accuracy of the traditional faults detection methods for the large-scale component software is not satisfactory. This paper proposes a large-scale software faults detection methods based on improved neural network combining the features of the large-scale software by computing the stable probability and building the neural network faults detection models. The proposed method can analyze the serial faults of the large-scale software to determine the positions of the faults. The experiment and simulation results show that the improved method for large-scale software fault detection can greatly improve the accuracy.

The Newly AND-OR FNN Modeling and Application

2012 ◽  
Vol 433-440 ◽  
pp. 846-852
Author(s):  
Jiang Hua Sui ◽  
Qiang Ma
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Self Organization ◽  
Rule Base ◽  
The Novel ◽  
Fuzzy Neural ◽  
Ship Control ◽  
Self Learning ◽  
Better Than

The novel multilayer feed-forward AND-OR fuzzy neural network (AND-OR FNN) is proposed in this paper. The main feature is shown not only in reducing the input space by special inner structure of neurons, but also auto-extracting the rules by the structure self-organization and parameter self-learning. The equivalent is proved that the network structure and fuzzy inference. The whole structure of network is optimized by genetic algorithm to extract if-then rules. This designing approach is employed to modeling an AND-OR FNN controller for ship control. Simulated results demonstrate that the number of rule base is decreased remarkably and the performance is much better than ordinary fuzzy control, illustrate the approach is practicable, simple and effective.

Study on the Combination of SOM and K-Means Algorithms in Manufacturing Process Quality Control

2013 ◽  
Vol 427-429 ◽  
pp. 1315-1318 ◽  
Author(s):  
Yi Bing Li ◽  
Fei Pan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Quality Control ◽  
Manufacturing Process ◽  
Low Cost ◽  
Research Topic ◽  
High Quality ◽  
Neural Network Algorithm ◽  
Som Neural Network

Nowadays, customers are seeking products of high quality and low cost. The use of neural networks in quality control has been a popular research topic over the last decade. An adaptive self-organizing mapping (SOM) neural network algorithm is proposed to overcome the shortages of traditional neural networks in this paper. In order to improve the classification effectiveness of SOM neural network, this paper designs an improved SOM neural network, which combined the SOM and K-means algorithms. The flow of combination of SOM and K-means algorithms was analyzed in this paper. And the case study of cement slide shoe bearing in manufacturing process was also given to illustrate the feasible and effective.

scholarly journals Review of Neural Network Algorithm and Its Application in Reactive Distillation

2021 ◽  
pp. 20-29
Author(s):  
Huihui Wang ◽  
Ruyang Mo
Keyword(s):  
Neural Network ◽  
Reactive Distillation ◽  
Research Progress ◽  
Paper Briefly ◽  
Network Algorithms ◽  
Highly Nonlinear ◽  
Learning Capabilities ◽  
Neural Network Algorithm ◽  
Self Learning ◽  
Nonlinear Robust

Artificial Neural Networks (ANN) can accurately identify and learn the potential relationship between input and output, and have self-learning capabilities and high fault tolerance, which can be used to predict or optimize the performance of complex systems. Reactive distillation integrates reaction and rectification into one device, so that the two processes occur at the same time and at the same place, but at the same time it also produces highly nonlinear robust behavior, making its process control and optimization unable to use conventional methods. Instead, neural network algorithms must be used. This paper briefly describes the research progress of neural network algorithms and reactive distillation technology, and summarizes the application of neural network algorithms in reactive distillation, aiming to provide reference for the development and innovation of industry technology.

scholarly journals Digital Image Based Identification of Rice Variety Using Image Processing and Neural Network

2015 ◽  
Vol 16 (1) ◽  
pp. 182
Author(s):  
Lilik Sumaryanti ◽  
Aina Musdholifah ◽  
Sri Hartati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Classification Accuracy ◽  
Rice Variety ◽  
Texture Features ◽  
Rice Varieties ◽  
Food Grains ◽  
Average Accuracy ◽  
Neural Network Algorithm

The increased of consumer concern on the originality of rice  variety and the quality of rice leads to originality certification of rice by existing institutions. Technology helps human to perform evaluations of food grains using images of objects. This study developed a system used as a tool to identify rice varieties. Identification process was performed by analyzing rice images using image processing. The analyzed features for identification consisted of six color features, four morphological features, and two texture features. Classifier used LVQ neural network algorithm. Identification results using a combination of all features gave average accuracy of 70,3% with the highest classification accuracy level of 96,6% for Mentik Wangi and the lowest classification accuracy of 30%  for Cilosari.

scholarly journals The application of neural network algorithm and embedded system in computer distance teach system

2022 ◽  
Vol 31 (1) ◽  
pp. 148-158
Author(s):  
Qin Qiu
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Learning Ability ◽  
Distance Teaching ◽  
Teaching System ◽  
Network Computer ◽  
Neural Network Algorithm ◽  
Bus Structure ◽  
Self Learning ◽  
Distance Education System

Abstract The computer distance teaching system teaches through the network, and there is no entrance threshold. Any student who is willing to study can log in to the network computer distance teaching system for study at any free time. Neural network has a strong self-learning ability and is an important part of artificial intelligence research. Based on this study, a neural network-embedded architecture based on shared memory and bus structure is proposed. By looking for an alternative method of exp function to improve the speed of radial basis function algorithm, and then by analyzing the judgment conditions in the main loop during the algorithm process, these judgment conditions are modified conditionally to reduce the calculation scale, which can double the speed of the algorithm. Finally, this article verifies the function, performance, and interface of the computer distance education system.

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