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  • In Jour Smart Sensing And Intelligent Systems

 

Research Article | 01-September-2014

RESEARCH ON THE CLASSIFICATION FOR FAULTS OF ROLLING BEARING BASED ON MULTI-WEIGHTS NEURAL NETWORK

be identified. Finally, simulations results based on real sampling data indicate the effectiveness of the methodology proposed in this paper. In addition, simulation results also indicate that MWNN utilized in this paper is more excellent than probabilistic neural network (PNN) and suitable for the classification of small samples.

Yujian Qiang, Ling Chen, Liang Hua, Juping Gu, Lijun Ding, Yuqing Liu

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1004–1023

Article | 01-September-2015

FRAGRANCE MEASUREMENT OF SCENTED RICE USING ELECTRONIC NOSE

into a sensor chamber; a water bath module for preparing rice sample, said water bath module including a heater attachment to facilitate cooking; a computing module to quantify the aroma data acquired by sensors; data acquisition module etc. Principal Component Analysis (PCA) implemented for clustering the data sets acquired from sensor array. Also data generated from sensor array was fed to Probabilistic Neural Network (PNN), Back-propagation Multilayer Perceptron (BPMLP) and Linear Discriminant

Arun Jana, Nabarun Bhattacharyya, Rajib Bandyopadhyay, Bipan Tudu, Subhankar Mukherjee, Devdulal Ghosh, Jayanta Kumar Roy

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1730–1747

Research Article | 12-September-2018

ECG Decision Support System based on feedforward Neural Networks

Abstract The success of an Electrocardiogram (ECG) Decision Support System (DSS) requires the use of an optimum machine learning approach. For this purpose, this paper investigates the use of three feedforward neural networks; the Multilayer Perceptron (MLP), the Radial Basic Function Network (RBF), and the Probabilistic Neural Network (PNN) for recognition of normal and abnormal heartbeats. Feature sets were based on ECG morphology and Discrete Wavelet Transformer (DWT) coefficients. Then, a

Hela Lassoued, Raouf Ketata, Slim Yacoub

International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–15

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