Distributed parallel deep learning of Hierarchical Extreme Learning Machine for multimode quality prediction with big process data

2019 ◽  
Vol 81 ◽  
pp. 450-465 ◽  
Author(s):  
Le Yao ◽  
Zhiqiang Ge
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Quality Prediction ◽  
Process Data ◽  
Learning Machine

scholarly journals Convolutional Extreme Learning Machines: A Systematic Review

2021 ◽  
Author(s):  
Iago Richard Rodrigues ◽  
Sebastião Rogério ◽  
Judith Kelner ◽  
Djamel Sadok ◽  
Patricia Takako Endo
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Research Area ◽  
New Paradigm ◽  
Learning Machines ◽  
Starting Point ◽  
Benchmark Datasets ◽  
Good Starting Point ◽  
Learning Machine

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

Dynamic Quality Prediction of Manufacturing Process Based on Extreme Learning Machine

2014 ◽  
Vol 889-890 ◽  
pp. 1231-1235
Author(s):  
Jun Guo ◽  
Yi Bing Li ◽  
Bai Gang Du
Keyword(s):  
Extreme Learning Machine ◽  
Manufacturing Process ◽  
Manufacturing Processes ◽  
Manufacturing Cost ◽  
Quality Prediction ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Learning Machine ◽  
Dynamic Quality ◽  
Quality Categories

In many manufacturing processes, the abnormal changes of some key process parameters could result in various categories of faulty products. In this paper, a machine learning approach is developed for dynamic quality prediction of the manufacturing processes. In the proposed model, an extreme learning machine is developed for monitoring the manufacturing process and recognizing faulty quality categories of the products being produced. The proposed model is successfully applied to a japanning-line, which improves the product quality and saves manufacturing cost.

Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study

2020 ◽  
Vol 13 (4) ◽  
pp. 1237-1250
Author(s):  
Deepak Kumar ◽  
Thendiyath Roshni ◽  
Anshuman Singh ◽  
Madan Kumar Jha ◽  
Pijush Samui
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Gaussian Process ◽  
Extreme Learning Machine ◽  
Groundwater Depth ◽  
Learning Machine

scholarly journals Wood Defect Detection Based on Depth Extreme Learning Machine

2020 ◽  
Vol 10 (21) ◽  
pp. 7488
Author(s):  
Yutu Yang ◽  
Xiaolin Zhou ◽  
Ying Liu ◽  
Zhongkang Hu ◽  
Fenglong Ding
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Defect Detection ◽  
Clustering Algorithm ◽  
Classification Performance ◽  
Image Features ◽  
Test Time ◽  
Machine Model ◽  
Feature Extraction Method ◽  
Learning Machine

The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm design of the wood images. The simple linear iterative clustering algorithm is used to improve the initial model; the obtained image features are used as ELM classification inputs. ELM has faster training speed and stronger generalization ability than other similar neural networks, but the random selection of input weights and thresholds degrades the classification accuracy. A genetic algorithm is used here to optimize the initial parameters of the ELM to stabilize the network classification performance. The depth extreme learning machine can extract high-level abstract information from the data, does not require iterative adjustment of the network weights, has high calculation efficiency, and allows CNN to effectively extract the wood defect contour. The distributed input data feature is automatically expressed in layer form by deep learning pre-training. The wood defect recognition accuracy reached 96.72% in a test time of only 187 ms.

scholarly journals Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shan Pang ◽  
Xinyi Yang
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Neuron Network ◽  
Learning Methods ◽  
Training Time ◽  
Fast Training ◽  
Handwritten Digit ◽  
Learning Machine ◽  
Hidden Layer ◽  
High Level

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

scholarly journals Deep Extreme Learning Machine and Its Application in EEG Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shifei Ding ◽  
Nan Zhang ◽  
Xinzheng Xu ◽  
Lili Guo ◽  
Jian Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Eeg Classification ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Learning Machine

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

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