Analog programmable neuron and case study on VLSI implementation of Multi-Layer Perceptron (MLP)

Heidari, Mahsa; Shamsi, Hossein

doi:10.1016/j.mejo.2018.12.007

Cited by 29 publications

(13 citation statements)

References 32 publications

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“…Back Propagation Neural Network (BPNN) is composed of Multi-Layer Perceptron (MLP) [41], [42] and Error Back Propagation (EBP or BP) [43], where the fundamental theorem is use Gradient Descent [44] to minimize the error function. Back Propagation Neural Network is a supervised learning network, which is also the most representative and widely applicable one among all the neural networks.…”

Section: F Back Propagation Neural Networkmentioning

confidence: 99%

Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System

Lin

et al. 2020

IEEE Access

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With the trend of Industry 4.0, the global machine tool industry is developing towards smart manufacturing. The ball bearing is a key component of the rotary axis of machine tool, and its functionality is to bear the external load on the axis as well as maintain the center position of the axis. A damaged bearing will result in abnormal vibration and noise, and thus will lead to the damage of the machine and produced workpieces. Therefore, inspection and identification of ball bearing failures is particularly important. This paper discusses the fault signals of ball bearings published by the Society for Machinery Failure Prevention Technology (MFPT) and creates a recognition model for the ball bearing state based on different fault states, and then we adopt two different approaches for feature extraction. The first approach implements Finite Impulse Response Filter (FIR) and Approximate Entropy (ApEn) to extract the signal features. The second approach utilizes the Chen-Lee chaotic system for analysis and takes its chaotic attractor as the feature of the state recognition. The comparison of model recognition accuracy for Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) was conducted after acquiring the features through the two approaches in this paper. The results of the experiments in this paper show that both of the feature extraction approaches enable the state to be recognized easily. The Chen-Lee chaotic system with BPNN not only reaches 100% identification rate and it has the highest overall efficiency; it takes only 0.054 second to complete the feature extraction for 63 sets of data; this study is able to provide timely and precise solution for the failure of key mechanical components. INDEX TERMS Ball bearing, back propagation neural network, support vector machine, fault detection, approximate entropy, Chen-Lee chaotic system.

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Section: F Back Propagation Neural Networkmentioning

confidence: 99%

Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System

Lin

et al. 2020

IEEE Access

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“…Multiple models are used to train the classifier for the same batch of data, and the final ensemble classifier is obtained by using the ensemble learning algorithm. At the same time, 6 common machine learning classification algorithms were selected for comparative experiments, including Naive Bayes (NB) [15] , Support Vector Machine (SVM) [16,17] , Logistic Regression (LR) [18] , Multilayer Perceptron (MLP) [19] , Deep Forest (GCForest) [20] , eXtreme Gradient boosting (XGBoost) [21] .…”

Section: Ensemble Analysis Of Clinical Datamentioning

confidence: 99%

Artificial Intelligence Applications in allergic rhinitis diagnosis: Focus on Ensemble Learning

Dai¹,

Meng

Tan

et al. 2020

Preprint

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Background and purpose: Artificial intelligence is an important product of the rapid development of computer technology today. This study intends to propose an intelligent diagnosis and detection method for AR based on ensemble learning. Method: This study collectedAR cases and other 7 types of diseases with similar symptoms:Rhinosinusitis, Chronic rhinitis, upper respiratory tract infection etc.) and collected clinical data such as medical history, clinical symptoms, allergen detection and imaging. Multiple models are used to train the classifier for the same batch of data, and the final ensemble classifier is obtained by using the ensemble learning algorithm. 5 common machine learning classification algorithms were selected for comparative experiments, including Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), Deep Forest (GCForest), eXtreme Gradient boosting (XGBoost). In order to evaluate the prediction results of AR samples, parameters such as Precision, Sensitivity, Specificity, G-Mean, F1-Score, and AUC under the ROC curve are jointly used as prediction evaluation indicators. Results: 7 classification models are used for comparison, covering probability model, tree model, linear model, ensemble model and neural network models, and the comprehensive classification evaluation index is lower than the ensemble classification algorithms ARF-OOBEE and GCForest. Compared with other algorithms, the accuracy of G-Mean and AUC parameters is improved nearly 2%, and it has good comprehensive classification characteristics for massive large data and unbalanced samples. Conclusion: The ensemble learning ARF-OOBEE model has good generalization performance and comprehensive classification ability to be used for diagnosis of AR.

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“…The Back Propagation Neural Network is a combination of Multilayer Perceptron (MLP) [33] and Back Propagation (BP) [34]. The Back Propagation Neural Network is a motoring type of learning network and is the most representative and widely applied neural network.…”

Section: The Aidm Classifier a Back Propagation Neural Networkmentioning

confidence: 99%

An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear

Jian

et al. 2020

IEEE Access

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Analog programmable neuron and case study on VLSI implementation of Multi-Layer Perceptron (MLP)

Cited by 29 publications

References 32 publications

Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System

Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System

Artificial Intelligence Applications in allergic rhinitis diagnosis: Focus on Ensemble Learning

An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear

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