Predicting Surface Roughness in Turning Operation Using Extreme Learning Machine

2014 ◽  
Vol 554 ◽  
pp. 431-435 ◽  
Author(s):  
Ahmad Nooraziah ◽  
V. Janahiraman Tiagrajah
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Extreme Learning Machine ◽  
Cutting Parameters ◽  
Feedforward Neural Network ◽  
Support Vector ◽  
Satisfactory Solution ◽  
Modeling Techniques ◽  
Learning Machine ◽  
Hidden Layer

Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection.

scholarly journals Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine

2014 ◽  
Vol 129 ◽  
pp. 428-436 ◽  
Author(s):  
Tiago Matias ◽  
Francisco Souza ◽  
Rui Araújo ◽  
Carlos Henggeler Antunes
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Feedforward Neural Network ◽  
Learning Machine ◽  
Hidden Layer

scholarly journals Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Wu ◽  
Xiaojuan Chen ◽  
Zhaohua Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Finite Impulse Response ◽  
Feedforward Neural Network ◽  
System Stability ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.

scholarly journals One-Class Classification with Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qian Leng ◽  
Honggang Qi ◽  
Jun Miao ◽  
Wentao Zhu ◽  
Guiping Su
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Classification Problem ◽  
Traditional Learning ◽  
Learning Speed ◽  
One Class Classifier ◽  
Learning Machine ◽  
Hidden Layer ◽  
One Class Classification ◽  
Network Approaches

One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.

scholarly journals Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242899
Author(s):  
Musatafa Abbas Abbood Albadr ◽  
Sabrina Tiun ◽  
Masri Ayob ◽  
Fahad Taha AL-Dhief ◽  
Khairuddin Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Ct Scan ◽  
Extreme Learning Machine ◽  
Experimental Result ◽  
Support Vector ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

scholarly journals Elliot and Symmetric Elliot Extreme Learning Machines for Gaussian Noisy Industrial Thermal Modelling

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 90 ◽  
Author(s):  
Jose Salmeron ◽  
Antonio Ruiz-Celma
Keyword(s):  
Extreme Learning Machine ◽  
Support Vector ◽  
Thermal Processes ◽  
Drying Process ◽  
Machine Model ◽  
Learning Machines ◽  
Nearest Neighbours ◽  
Thermal Drying ◽  
Learning Machine ◽  
Hidden Layer

This research proposes an Elliot-based Extreme Learning Machine approach for industrial thermal processes regression. The main contribution of this paper is to propose an Extreme Learning Machine model with Elliot and Symmetric Elliot activation functions that will look for the fittest number of neurons in the hidden layer. The methodological proposal is tested on an industrial thermal drying process. The thermal drying process is relevant in many industrial processes such as the food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries and others. The methodological proposal of this paper outperforms the following techniques: Linear Regression, k-Nearest Neighbours regression, Regression Trees, Random Forest and Support Vector Regression. In addition, all the experiments have been benchmarked using four error measurements (MAE, MSE, MEADE, R 2 ).

AUTOMATIC DETECTION OF CARDIOVASCULAR DISEASE USING DEEP KERNEL EXTREME LEARNING MACHINE

2018 ◽  
Vol 30 (06) ◽  
pp. 1850038
Author(s):  
Dongping Li
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Feature Extraction ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Process ◽  
Support Vector ◽  
Learning Models ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

The electrocardiogram (ECG) is a principal signal employed to automatically diagnose cardiovascular disease in shallow and deep learning models. However, ECG feature extraction is required and this may reduce diagnosis accuracy in traditional shallow learning models, while backward propagation (BP) algorithm used by the traditional deep learning models has the disadvantages of local minimization and slow convergence rate. To solve these problems, a new deep learning algorithm called deep kernel extreme learning machine (DKELM) is proposed by combining the extreme learning machine auto-encoder (ELM-AE) and kernel ELM (KELM). In the new DKELM architecture with [Formula: see text] hidden layers, ELM-AEs are employed by the front [Formula: see text] hidden layers for feature extraction in the unsupervised learning process, which can effectively extract abstract features from the original ECG signal. To overcome the “dimension disaster” problem, the kernel function is introduced into ELM to act as classifier by the [Formula: see text]th hidden layer in the supervised learning process. The experiments demonstrate that DKELM outperforms the BP neural network, support vector machine (SVM), extreme learning machine (ELM), deep auto-encoder (DAE), deep belief network (DBN) in classification accuracy. Though the accuracy of convolutional neural network (CNN) is almost the same as DKELM, the computing time of CNN is much longer than DKELM.

Prediction of two-dimensional topography of laser cladding based on neural network

2019 ◽  
Vol 33 (01n03) ◽  
pp. 1940034 ◽  
Author(s):  
Li Hongyu ◽  
Chen Hui ◽  
Wu Ying ◽  
Chen Yong ◽  
Yi Wei
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Back Propagation ◽  
Feedforward Neural Network ◽  
Two Dimensional ◽  
Back Propagation Algorithm ◽  
Cladding Layer ◽  
Propagation Algorithm ◽  
Learning Machine ◽  
Hidden Layer

The two-dimensional morphology of the cladding layer has an important influence on the quality of the cladding layer and the crack tendency. Using the powerful nonlinear processing ability of the single hidden layer feedforward neural network, a prediction model between the cladding technological parameters and the two-dimensional morphology of the cladding layer is established. Taking the cladding parameters as the input and the two-dimensional morphology of the cladding as the output, the experimental data is used to train the network to achieve a high-level mapping of the input and output. On this basis, the algorithm of extreme learning machine is used to optimize the single hidden layer feedforward neural network to overcome the problems of slow convergence speed, more network training parameters and easy local convergence in back-propagation algorithm. The results show that the relationship between the cladding process parameters and the two-dimensional morphology of the cladding layer can be roughly reflected by the back-propagation algorithm. However, the prediction results are not stable and the error rate is between 10% and 40%. The neural network optimized by the extreme learning machine is utilized to get a better prediction result. The error rate is 10–20%.

scholarly journals An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

2021 ◽  
Vol 38 (4) ◽  
pp. 1229-1235
Author(s):  
Derya Avci ◽  
Eser Sert
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Optical Illusions ◽  
Factors Affecting ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Elm Classifier

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

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