2017
DOI: 10.1007/s40747-017-0054-8
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Restricted Boltzmann machine and softmax regression for fault detection and classification

Abstract: A unique technique is proposed based on restricted Boltzmann machine (RBM) and softmax regression for automated fault detection and classification using the acoustic signal generated from IC (Internal Combustion) engines. This technique uses RBM for unsupervised fault feature extraction from the frequency spectrum of the noisy acoustic signal. These extracted features are then used to reduce the dimensionality of the training and testing data vectors. These reduced dimensionality data vectors are used by softm… Show more

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Cited by 22 publications
(12 citation statements)
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“…The combination of LR and RBM is actually widely used in face and handwriting recognition (e.g. Chopra & Yadav 2017). The improvement of the combination of RBM is rather significant while using 'pixel input' because of the characteristics of neural networks (See section 4).…”
Section: K-nearest Neighbours (Knn)mentioning
confidence: 99%
“…The combination of LR and RBM is actually widely used in face and handwriting recognition (e.g. Chopra & Yadav 2017). The improvement of the combination of RBM is rather significant while using 'pixel input' because of the characteristics of neural networks (See section 4).…”
Section: K-nearest Neighbours (Knn)mentioning
confidence: 99%
“…The term softmax itself has been first introduced by Bridle in neural networks, where it is usually employed as an activation function to normalise data [52]. In computer science, applications of softmax are varied: classification methods (again, softmax regression) for supervised and unsupervised learning [53][54][55], computer vision [56][57][58], reinforcement learning [59][60][61] and hardware design [62], just to name some current areas of application. Additionally, a considerable number of conference papers is witnessing the popularity of softmax and its proposed variants [63][64][65][66][67].…”
Section: Plos Onementioning
confidence: 99%
“…Softmax classifier is selected as the output function of the network to predict the defect category of the zone box [25]. The Softmax layer follows the full connection layer and receives the output of full connection layer, then calculates the prediction probability distribution of the region as follows: puifalse|m=emikNcemk,where, p ( u i | m ) represents the probability of category u i under the condition of softmax layer input feature vector m ; m i represents the i th input data of the eigenvector m ; N c represents the total number of defect categories.…”
Section: An Improved Fast‐rcnn Visual Target Detection Algorithmmentioning
confidence: 99%