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An evolutionary ensemble convolutional neural network for fault diagnosis problem
Mohammad Tayarani
Centre for AI and Robotics Research
School of Physics, Engineering & Computer Science
Department of Computer Science
Biocomputation Research Group
Research output
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peer-review
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Keyphrases
Automatic Fault Diagnosis
25%
Benchmark Problems
25%
Convolutional Neural Network
100%
Diagnosis Problems
100%
Ensemble Convolutional Neural Networks
100%
Ensemble Machine Learning
25%
Evolutionary Algorithms
100%
Evolutionary Ensemble
100%
Extracting Features
25%
Fault Diagnosis
100%
Feature Extraction Methods
25%
Fourier
25%
Global Search
25%
Gradient Descent
25%
Gradient Method
25%
Learning Algorithm
25%
Local Optimum
25%
Statistical Features
25%
Transform Function
25%
Vibration Analysis
25%
Vibration Data
50%
Voting System
25%
Wavelet
25%
Weighted Voting
25%
Computer Science
Benchmark Problem
20%
Convolutional Neural Network
100%
Diagnose Problem
100%
Evolutionary Algorithms
80%
Experimental Result
20%
Fault Diagnosis
100%
Feature Extraction
20%
Frequency Domain
20%
Gradient Descent
40%
Learning Algorithm
20%
Machine Learning Algorithm
20%
Statistical Feature
20%