Faults classification of asynchronous machine based on the probabilistic neural network (PNN)

2016 ◽  
Author(s):  
Rahma Ouhibi ◽  
Salma Bouslama ◽  
Kaouther Laabidi
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Asynchronous Machine

scholarly journals Pattern Classification of Fabric Defects Using a Probabilistic Neural Network and Its Hardware Implementation using the Field Programmable Gate Array System

2017 ◽  
Vol 25 (0) ◽  
pp. 42-48 ◽  
Author(s):  
Abul Hasnat ◽  
Anindya Ghosh ◽  
Amina Khatun ◽  
Santanu Halder
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Probabilistic Neural Network ◽  
Clock Period ◽  
Data Set ◽  
Gate Arrays ◽  
Hardware System ◽  
Field Programmable ◽  
Programmable Gate Arrays

This study proposes a fabric defect classification system using a Probabilistic Neural Network (PNN) and its hardware implementation using a Field Programmable Gate Arrays (FPGA) based system. The PNN classifier achieves an accuracy of 98 ± 2% for the test data set, whereas the FPGA based hardware system of the PNN classifier realises about 94±2% testing accuracy. The FPGA system operates as fast as 50.777 MHz, corresponding to a clock period of 19.694 ns.

The Classification of Non-Asbestos Gasket Formulation Using Clustering Method

2012 ◽  
Vol 233 ◽  
pp. 388-391
Author(s):  
Mei Hong Liu ◽  
Zhen Hua Li ◽  
Yu Xian Li ◽  
Jun Ruo Chen
Keyword(s):  
Neural Network ◽  
Raw Materials ◽  
Probabilistic Neural Network ◽  
Well Performance ◽  
Clustering Problem ◽  
Network Method ◽  
Reduce Development Time ◽  
Som Neural Network ◽  
Optimal Formula

At present, study on the non-asbestos gasket materials is the hotspot research in static sealing field. The non-asbestos sealing gaskets research and development has made great strides into the practical phase. Formula is an important factor of material, which determines performance of material. In order to obtain well performance, it is needed to optimization formula to get optimal formula that not only improve performance of non-asbestos gasket, but also reduce development time accordingly reduce cost of non-asbestos gasket. Classification of raw materials can be transformed into a mathematical clustering problem. It means that according to some algorithm, there will be some sort of input values of similar links together. Many neural networks were widely used in the classification of different materials. A method of classification by using neural network to the known 15 kinds of the non-asbestos gaskets of formula data was proposed in this paper. By using the PNN (probabilistic neural network), LVQ(Learning Vector Quantization) neural network and SOM (Self-Organizing Feature Map) neural network respectively to classify the non-asbestos gaskets to find a suitable method in the classification of non-asbestos gaskets formula. The results indicated that PNN neural network and LVQ neural network method based on the data that provided in the paper both can effectively classify, while SOM neural network can not classify them ideally, thus it provides a new theoretical basis for the classification of the non-asbestos gaskets.

PROBABILISTIC NEURAL NETWORK AND ITS ADAPTIVE VERSION — A STOCHASTIC APPROACH TO PD PATTERN CLASSIFICATION TASK

2005 ◽  
Vol 02 (04) ◽  
pp. 333-344 ◽  
Author(s):  
B. KARTHIKEYAN ◽  
S. GOPAL ◽  
S. VENKATESH
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Probabilistic Neural Network ◽  
Stochastic Approach ◽  
Diagnostic Techniques ◽  
Single Type ◽  
Destructive Testing ◽  
Theoretical Concepts

The quality of electrical insulation of any power apparatus is an indispensable requirement for its successful and reliable operation. Partial Discharge (PD) phenomenon serves as an effective Non Destructive Testing (NDT) technique and provides an index on the quality of the insulation. The innovative trend of using Artificial Neural Network (ANN) towards the classification of PD patterns is cogent and discernible. In this paper a novel method for the classification of the PD patterns using the original Probabilistic Neural Network (PNN) as well as its variation is elucidated. A preprocessing scheme that extracts pertinent features of PD from the raw data towards achieving a compact ANN has been employed. The classification of single-type insulation defects such as voids, surface discharges and corona has been taken up. The first part of the paper gives a brief on PD, various diagnostic techniques and interpretation. The second part deals with the theoretical concepts of PNN and its adaptive version. The last part provides details on various results and comparisons of the PNN and its adaptive version in PD pattern classification.

Classification of Partial Discharge Signals using Probabilistic Neural Network

2007 ◽  
Author(s):  
Demetres Evagorou ◽  
Andreas Kyprianou ◽  
Paul L. Lewin ◽  
Andreas Stavrou ◽  
Venizelos Efthymiou ◽  
...  
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Partial Discharge

Application of Probabilistic Neural Network in Pattern Classification

2013 ◽  
Vol 441 ◽  
pp. 738-741 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Classification ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Networks ◽  
Training Samples ◽  
The One ◽  
Speed Accuracy

The network model of probabilistic neural network and its method of pattern classification and discrimination are first introduced in this paper. Then probabilistic neural network and three usually used back propagation neural networks are established through MATLAB7.0. The pattern classification of dots on a two-dimensional plane is taken as an example. Probabilistic neural network and improved back propagation neural networks are used to classify these dots respectively. Their classification results are compared with each other. The simulation results show that compared with back propagation neural networks, probabilistic neural network has simpler learning rules, faster training speed and it needs fewer training samples; the pattern classification method based on probabilistic neural network is very effective, and it is superior to the one based on back propagation neural networks in classifying speed, accuracy as well as generalization ability.

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