Extreme Learning Machines [Trends &amp; Controversies]

Cambria, Erik; Huang, Guang-Bin; Kasun, Liyanaarachchi Lekamalage Chamara; Zhou, Hongming; Vong, Chi‐Man; Lin, Jiarun; Yin, Jianping; Cai, Zhiping; Liu, Qiang; Li, Kuan; Leung, Victor C. M.; Liang, Feng; Ong, Yew-Soon; Lim, Meng‐Hiot; Akusok, Anton; Lendasse, Amaury; Corona, Francesco; Nian, Rui; Miché, Yoan; Gastaldo, Paolo; Zunino, Rodolfo; Decherchi, Sergio; Yang, Xuefeng; Mao, Kezhi; Oh, Beom-Seok; Jeon, Jehyoung; Toh, Kar-Ann; Teoh, Andrew Beng Jin; Kim, Jaihie; Yu, Hanchao; Chen, Yiqiang; Liu, Junfa

doi:10.1109/mis.2013.140

Cited by 359 publications

(74 citation statements)

References 4 publications

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“…This motivated investigation of longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (inference), and geometrical connections between representation learning, manifold learning, and density estimation. Later, Cambria et al [42] interpreted the ELM method as an emerging learning technique that provides efficient unified solutions to generalized feed-forward networks, including, but not limited to (single-and multi-hidden-layer) NNs, radial basis function networks, and kernel learning.…”

Section: The Other Methodsmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

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Section: The Other Methodsmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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“…The parameters of the hidden layer are independent upon the target function and the training dataset [39,40]. The output weights which link the hidden layer to output layer are determined analytically through a Moore-Penrose generalized inverse [37,41]. Benefited from its simple structure and efficient learning algorithm, ELM owns very good generalization capability superior to the traditional ANN and SVM.…”

Section: Extreme Learning Machines Based Autoencodermentioning

confidence: 99%

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

2016

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Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some "insignificant' feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

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“…Kasun et al [2] proposed a multilayer ELM (ML-ELM) based on the idea of autoencoding. That is, extract higher order features by reducing the high dimensional input data to a lower dimensional feature space similar to CNN.…”

Section: Multilayer Elmmentioning

confidence: 99%

Fast Handwritten Digit Recognition with Multilayer Ensemble Extreme Learning Machine

Noushahr

Ahmadi

Casey

2015

Research and Development in Intelligent Systems XXXII

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Conventional artificial neural networks and convolutional neural networks perform well on the task of automatic handwriting recognition. But, they suffer from long training times and their complex nature. An alternative learning algorithm called Extreme Learning Machine overcomes these shortcomings by determining the weights of a neural network analytically. In this paper, a novel classifier based on Extreme Learning Machine is proposed that achieves competitive accuracy results while keeping training times low. This classifier is called multilayer ensemble Extreme Learning Machine. The novel classifier is evaluated against traditional backpropagation and Extreme Learning Machine on the well-known MNIST dataset. Possible future work on parallel Extreme Learning Machine is shown up.

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Extreme Learning Machines [Trends & Controversies]

Cited by 359 publications

References 4 publications

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

Fast Handwritten Digit Recognition with Multilayer Ensemble Extreme Learning Machine

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