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135 results on '"probabilistic neural network"'

2. I-PNN: An Improved Probabilistic Neural Network for Binary Classification of Imbalanced Medical Data

Author

Izonin, Ivan, Tkachenko, Roman, Greguš, Michal, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Strauss, Christine, editor, Cuzzocrea, Alfredo, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2022
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3. Rice Plant Disease Detection Using Image Processing and Probabilistic Neural Network

Author

Ökten, İrfan, Yüzgeç, Uğur, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, and Seyman, Muhammet Nuri, editor

Published
2022
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4. Investigation of Results Using Various Databases and Algorithms for Music Player Using Speech Emotion Recognition

Author

Deshmukh, Shrikala, Gupta, Preeti, Mane, Prashant, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Engelbrecht, Andries, editor, Scotti, Fabio, editor, Gandhi, Niketa, editor, Manghirmalani Mishra, Pooja, editor, Fortino, Giancarlo, editor, Sakalauskas, Virgilijus, editor, and Pllana, Sabri, editor

Published
2022
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9. Adaptive Learning of Probabilistic Neural Network in Situation of Overlapping Classes in Classification Task

Author

Bodyanskiy, Yevgeniy, Deineko, Anastasiia, Pliss, Iryna, Chala, Olha, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Shakhovska, Natalya, editor, and Medykovskyy, Mykola O., editor

Published
2021
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10. On the Possibility of Using Neural Networks for the Thunderstorm Forecasting

Author

Stankova, Elena, Tokareva, Irina O., Dyachenko, Natalia V., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, and Torre, Carmelo Maria, editor

Published
2021
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11. Lung Boundary Detection and Classification in Chest X-Rays Images Based on Neural Network

Author

Hamad, Yousif A., Simonov, Konstantin, Naeem, Mohammad B., Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Khalaf, Mohammed I., editor, Al-Jumeily, Dhiya, editor, and Lisitsa, Alexei, editor

Published
2020
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14. Ada-Boosted Locally Enhanced Probabilistic Neural Network for IoT Intrusion Detection

Author

Jan, Tony, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Javaid, Nadeem, editor, Ikeda, Makoto, editor, and Takizawa, Makoto, editor

Published
2019
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15. Machine Learning Approach for Reliability Assessment of Open Source Software

Author

Behera, Ranjan Kumar, Rath, Santanu Kumar, Misra, Sanjay, Leon, Marcelo, Adewumi, Adewole, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Misra, Sanjay, editor, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Stankova, Elena, editor, Korkhov, Vladimir, editor, Torre, Carmelo, editor, Rocha, Ana Maria A.C., editor, Taniar, David, editor, Apduhan, Bernady O., editor, and Tarantino, Eufemia, editor

Published
2019
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16. Generating Random Variates via Kernel Density Estimation and Radial Basis Function Based Neural Networks

Author

Candia-García, Cristian, Forero, Manuel G., Herrera-Rivera, Sergio, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Vera-Rodriguez, Ruben, editor, Fierrez, Julian, editor, and Morales, Aythami, editor

Published
2019
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17. A Machine Learning Based Engine Error Detection Method

Author

Cheng, Xinsong, Zhao, Liang, Lin, Na, Gong, Changqing, Wang, Ruiqing, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin Sherman, Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert Y., Series editor, Long, Keping, editor, Leung, Victor C.M., editor, Zhang, Haijun, editor, Feng, Zhiyong, editor, Li, Yonghui, editor, and Zhang, Zhongshan, editor

Published
2018
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18. Detection of Depression from Brain Signals: A Review Study

Author

Kaur, Prabhjyot, Siuly, Siuly, Miao, Yuan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Siuly, Siuly, editor, Lee, Ickjai, editor, Huang, Zhisheng, editor, Zhou, Rui, editor, Wang, Hua, editor, and Xiang, Wei, editor

Published
2018
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22. Time Series Prediction for Electric Power Industry with the Help of Syntactic Pattern Recognition

Author

Flasiński, Mariusz, Jurek, Janusz, Peszek, Tomasz, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chmielewski, Leszek J., editor, Datta, Amitava, editor, Kozera, Ryszard, editor, and Wojciechowski, Konrad, editor

Published
2016
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25. Sequential Hierarchical Image Recognition Based on the Pyramid Histograms of Oriented Gradients with Small Samples

Author

Savchenko, Andrey V., Milov, Vladimir R., Belova, Natalya S., Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Khachay, Mikhail Yu., editor, Konstantinova, Natalia, editor, Panchenko, Alexander, editor, Ignatov, Dmitry, editor, and Labunets, Valeri G., editor

Published
2015
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26. Teaching a Virtual Robot to Perform Tasks by Learning from Observation

Author

Tîrnăucă, Cristina, Montaña, José L., Ortiz–Sobremazas, Carlos, Ontañón, Santiago, González, Avelino J., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, García-Chamizo, Juan M., editor, Fortino, Giancarlo, editor, and Ochoa, Sergio F., editor

Published
2015
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29. Texture-Based Leaf Identification

Author

Sulc, Milan, Matas, Jiri, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Agapito, Lourdes, editor, Bronstein, Michael M., editor, and Rother, Carsten, editor

Published
2015
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31. Wrapping Things Up...

Author

Ferreira, João Filipe, Dias, Jorge, Siciliano, Bruno, Series editor, Khatib, Oussama, Series editor, Ferreira, João Filipe, and Miranda Dias, Jorge

Published
2014
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35. Application of Fuzzy Techniques in Human-Robot Interaction - A Review

Author

Kuo, I-Han, Jayawardena, Chandimal, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Beetz, Michael, editor, Johnston, Benjamin, editor, and Williams, Mary-Anne, editor

Published
2014
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36. Nonlinear Transformation of the Distance Function in the Nearest Neighbor Image Recognition

Author

Savchenko, Andrey V., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Zhang, Yongjie Jessica, editor, and Tavares, João Manuel R. S., editor

Published
2014
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41. Introduction to Sequential Heteroscedastic Probabilistic Neural Networks

Author

Kurosh Madani, Ali Mahmoudi, and Reza Askari Moghadam

Subjects
Heteroscedasticity, Artificial neural network, business.industry, Computer science, Probabilistic logic, Process (computing), Distinctive feature, Machine learning, computer.software_genre, Probabilistic neural network, Artificial intelligence, Sequence learning, business, computer, Interpretability
Abstract

This paper is dedicated to analyzing the performance of an online classification algorithm called sequential heteroscedastic probabilistic neural network (SHPNN). The aforementioned algorithm is a variant of probabilistic neural networks (PNNs). This algorithm has the advantage of being structurally flexible to match the complexities of the data space. Another distinctive feature of this algorithm is the fact that it can achieve roughly the same level of accuracy compared to its counterparts while having an acceptable speed in training phase. But perhaps its most important quality is the fact that SHPNN’s structure contains valuable information about the underlying statistics of the data. In this paper the derivation of SHPNN formulation is presented and discussed. The following analysis includes comparison between the performance of SHPNN and other similar algorithms. Furthermore, the interpretability of this network is analyzed by visualizing the learning process.

Published
2021
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42. On the Possibility of Using Neural Networks for the Thunderstorm Forecasting

Author

Irina O. Tokareva, Elena N. Stankova, and Natalia V. Dyachenko

Subjects
Probabilistic neural network, Relation (database), Artificial neural network, Computer science, Simple (abstract algebra), Probabilistic logic, Thunderstorm, Weather forecasting, Radial basis function, Data mining, computer.software_genre, computer, Physics::Atmospheric and Oceanic Physics
Abstract

The paper explores the possibility of forecasting such dangerous meteorological phenomena as a thunderstorm by applying five types of neural network to the output data of a hydrodynamic model that simulates dynamic and microphysical processes in convective clouds. The ideas and the result delivered in [1] are developed and supplemented by the classification error calculations and by consideration of radial basic and probabilistic neural networks. The results show that forecast accuracy of all five networks reaches values of 90%. However, the radial basis function has the advantages of the highest accuracy along with the smallest classification error. Its simple structure and short training time make this type of neuralnetwork the best one in view of accuracy versus productivity relation.

Published
2021
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43. Feature Extraction of Coronavirus X-Ray Images by RNN, Correlational Networks, and PNN

Author

V. Kakulapati and Appiah Prince

Subjects
Coronavirus disease 2019 (COVID-19), Artificial neural network, Computer science, business.industry, Deep learning, Feature extraction, medicine.disease_cause, Machine learning, computer.software_genre, Correlation, Probabilistic neural network, Recurrent neural network, medicine, Artificial intelligence, business, computer, Coronavirus
Abstract

Today, the entire world is suffering and fear with the epidemic of coronavirus. The statistics of coronavirus as on current data, 213 countries affected by this pandemic, more than 1.1 cr people, suffering from this killer virus, and about 6L fatality are recording. This virus is spreading speedily, and the patients are mainly suffering from breathing. The patient having previous health issues will get more possibility of this disease. In this work, try to evaluate the COVID 19 patient x-ray images by using DL (deep learning) techniques developed on the grouping of a recurrent neural network (RNN) and a correlational network to identify COVID-19 automatically is used in the prediction of high-risk outbreaks to learn on the prognostic code sequences of patients. By use of RNNs helps the model to assess difference in patient status in terms of time and thereby improve predictive precision. A correlational neural network is to identify the salient features for CORONAVIRUS, and these features are feed into a Probabilistic neural network (PNN) for better corona diagnosis. The experimental result gives improved accuracy for analyzing coronavirus disease.

Published
2021
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44. Automated Breast Cancer Diagnosis Based on Neural Network Algorithms

Author

Lalita Sharma, Kamran Alam, and Namarta Chopra

Subjects
Artificial neural network, medicine.diagnostic_test, Computer science, medicine.disease, Probabilistic neural network, Bayes' theorem, Breast cancer, Node (computer science), medicine, Mammography, Microcalcification, Layer (object-oriented design), medicine.symptom, Algorithm
Abstract

Breast cancer, the most common among the women, is primarily examined visually by starting with initial screening of breast and lymph nodes in armpit, dermoscopic analysis, histopathological tests and a biopsy. In India as per the ratings, 25–35% women are affected with breast cancer in every city in India. Being in a developing country like India, majority of women are dying due to breast cancer and for every 4 min women is affected with the same. Women are suffering a lot due to this curse, masses or micro calcification are the symptoms a woman can have at a previous stage, where microcalcification is mineral which gets into the breast tissue. It’s quite hard to examine due to ultra-fine shapes and uncertain edges. It is being categorized into three main categories normal, malignant and Benign, which can be detected using proposed system. In proposed scheme, input images are being compared with large collection of databases with the replacement of sigmoid activation function. Probabilistic neural network is used to describe nonlinear statement limits which further leads to Bayes optimal and also all the function which bear same properties as well. Any input data or algorithm can be pointed with the four layer neural network. Standard PNN consists of input layer of N nodes, a pattern layer of m nodes, a summation layer of k node and it consists of pseudo layer L nodes, which is used for decision making which is also known as decision layer for producing output.

Published
2021
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46. Adaptive Learning of Probabilistic Neural Network in Situation of Overlapping Classes in Classification Task

Author

Yevgeniy Bodyanskiy, Iryna Pliss, Anastasiia Deineko, and Olha Chala

Subjects
Probabilistic neural network, Artificial neural network, Computer science, business.industry, Sliding window protocol, Data classification, Probabilistic logic, Feature (machine learning), Artificial intelligence, Adaptive learning, business, Fuzzy logic
Abstract

The adaptive probabilistic neural networks for classification task in situation of overlapping classes is proposed. This network is designed to solve data classification task when data are fed sequentially in the online mode, and forming classes are mutually overlapped - the fuzzy case. The distinct feature of the network is that the learning process of the pattern layer uses the sliding window. This allows us to keep the constant number of neurons in this layer. Another point of the learning process is the tuning ability of activation functions’ widespread parameters in online mode. The described advantage allows us to improve the classification quality. Last but not least is the ability to compute both the probability and membership levels of each observation to each of forming classes. The proposed adaptive probabilistic neural network with fuzzy interference is simple in numerical implementation and has high learning speed. The results of experiments confirmed the correctness of approach under consideration.

Published
2020
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47. Classification of Seagrass Habitat Using Probabilistic Neural Network

Author

Prajna Tantry, Anand Upadhyay, and Aarohi Varade

Subjects
Shore, geography, education.field_of_study, Geospatial analysis, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, biology, Population, 0211 other engineering and technologies, 02 engineering and technology, computer.software_genre, biology.organism_classification, 01 natural sciences, Probabilistic neural network, Seagrass, Habitat, Environmental science, RGB color model, Marine ecosystem, education, computer, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

Seagrasses are very valuable asset which maintains the ecological and economic components of marine ecosystem. Seagrasses provides Food and shelter for marine animals, purifies water, provides nutrients, and decreases the speed of storm. As the human population is increasing, various types of threads on the seagrass is also increasing. Different activities of humans like sewage input, dumping of solid waste on the shoreline and anchoring of boats are main reasons of reduction in the population of seagrass. Remote Sensing is a technique through which the geospatial data of any location can be captured. So for the proposed research the remotely sensed Seagrass image of Andaman & Nicobar Island of India is collected using Google Earth. The image collected from Google Earth is high resolution image. High resolution image contains features in the form of RGB value. Probabilistic Neural Network (PNN) algorithm is a machine learning algorithm which is used for the classification of Seagrass from the data. Algorithm is applied on the extracted RGB value of the image. After applying PNN algorithm on the remotely sensed data, the classification of seagrass successfully performed with the accuracy of 99% and the Kappa Coefficient value is 0.99. The result shows the very good accuracy of the classification.

Published
2020
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48. Unsupervised Method Based on Superpixel Segmentation for Corpus Callosum Parcellation in MRI Scans

Author

Walid Barhoumi, Amal Jlassi, Khaoula ElBedoui, and Chokri Maktouf

Subjects
Superpixel segmentation, Computer science, business.industry, Principle of maximum entropy, Pattern recognition, 02 engineering and technology, Corpus callosum, 03 medical and health sciences, Probabilistic neural network, Mri image, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Cluster analysis, business, 030217 neurology & neurosurgery
Abstract

In this paper, we introduce an unsupervised method for the parcellation of the Corpus Callosum (CC) from MRI images. Since there are no visible landmarks within the structure that explicit its parcels, non-geometric CC parcellation is a challenging task especially that almost of proposed methods are geometric or data-based. In fact, in order to subdivide the CC from brain sagittal MRI scans, we adopt the probabilistic neural network as a clustering technique. Then, we use a cluster validity measure based on the maximum entropy (Vmep) to obtain the optimal number of classes. After that, we obtain the isolated CC that we parcel automatically using SLIC (Simple Linear Iterative Clustering) as superpixel segmentation technique. The obtained results on two challenging public datasets prove the performance of the proposed method against geometric methods from the state of the art. Indeed, as best as we know, it is the first work that investigates the validation of a CC parcellation method on ground-truth datasets using many objective metrics.

Published
2020
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49. A Recognition Method via Improved CEEMDAN and Multiscale Entropy for Enhancing the Diagnostic Accuracy

Author

Wenjuan Wang, Xuejiao Chen, Feng Ding, and Jianhui Tian

Subjects
Probabilistic neural network, Signal processing, Noise (signal processing), Feature (computer vision), Computer science, business.industry, Feature extraction, Pattern recognition, Artificial intelligence, Entropy (energy dispersal), Fault (power engineering), business, Hilbert–Huang transform
Abstract

In order to solve the problem of non-stationary, non-linear and feature extraction difficulties of the vibration signal of rolling bearing, this paper puts forward a recognition method based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multiscale entropy for enhancing the accuracy of rolling bearing fault diagnosis. To effectively extract fault-related information of vibration signals of rolling bearing, the improved CEEMDAN is used for signal processing to obtain a set of intrinsic mode function (IMF). The first six IMF with the most fault information is selected, and the multiscale entropy (MSE) method is used to extract the fault feature. Also, the extracted eigenvectors are input to the probabilistic neural network (PNN) to realize fault diagnosis. To prove the advantages of the improved CEEMDAN-MSE method, it is compared with empirical mode decomposition-MSE (EMD-MSE), ensemble empirical mode decomposition-MSE (EEMD-MSE), CEEMDAN-MSE, improved CEEMDAN-approximate entropy (improved CEEMDAN-AE) and improved CEEMDAN-sample entropy (improved CEEMDAN-SE) methods. The experimental result shows that the method greatly enhances the diagnostic accuracy and highlights the advantages of the method.

Published
2020
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50. Application of the Empirical Mode Decomposition to Detect Fault in Power Electronic Converter

Author

Soyel Ghosh, Sankhadip Saha, and Dibyendu Khan

Subjects
Root mean square, Support vector machine, Probabilistic neural network, Wavelet, Artificial neural network, Computer science, business.industry, Pattern recognition, Artificial intelligence, Perceptron, business, Hilbert–Huang transform, Standard deviation
Abstract

This paper presents a technique for the feature extraction of voltage signal for classification of open-circuited (O-C) faults in power electronic converters (PECs) connected to grid tied wind power system. The Empirical Mode Decomposition (EMD) was applied in the PEC output for the detection of the fault, as well as identification of the abnormal power switching device in the PEC. This method is applied on 12 types of signals including normal and faulted output voltage signals. Six features are computed from each Intrinsic Mode Functions (IMFs) obtained from EMD which are: Root Mean Square (RMS), Mean, Median, Variation, Standard Deviation and Entropy. The classification of the fault is achieved using Neural Networks (NN) and a comparison of classification precision between Probabilistic Neural Network (PNN) and Back-Propagation Multi-Layer perceptron (BPMLP) is also done. The overall classification accuracy concludes that EMD allows receiving better performance in the fault classification than other traditional methods like support vector machines, Wavelet.

Published
2020
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