Fault diagnosis model based on Granular Computing and Echo State Network

Lü, Cheng; Xu, Peng; Cong, Linhu

doi:10.1016/j.engappai.2020.103694

Cited by 11 publications

(3 citation statements)

References 30 publications

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“…The method employed a sparse regression with the L 1 2 regularization, having properties of unbiasedness and sparsity, and the L 2 regularization, having ability on shrinking the amplitude of the output weights. Meanwhile, the L 1 2 norm regularization term could also used to overcome the iterative numerical oscillation problem, described in [89,90].…”

Section: Designs Of Regularization and Training Phasementioning

confidence: 99%

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A Review of Designs and Applications of Echo State Networks

Sun¹,

Song²,

Hong³

et al. 2020

Preprint

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Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial, medical, economic and linguistic. Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs. ESN, with a strong theoretical ground, is practical, conceptually simple, easy to implement. It avoids non-converging and computationally expensive in the gradient descent methods. Since ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced Deep ESN model opened the way to uniting the merits of deep learning and ESNs. Besides, the combinations of ESNs with other machine learning models have also overperformed baselines in some applications. However, the apparent simplicity of ESNs can sometimes be deceptive and successfully applying ESNs needs some experience. Thus, in this paper, we categorize the ESN-based methods to basic ESNs, DeepESNs and combinations, then analyze them from the perspective of theoretical studies, network designs and specific applications. Finally, we discuss the challenges and opportunities of ESNs by summarizing the open questions and proposing possible future works.

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Section: Designs Of Regularization and Training Phasementioning

confidence: 99%

“…ESN-based models have also been used in manufacturing, such as motor control [218], detection and diagnosis of anomaly and fault [219,220,221,222,106,223,89,72], production system monitor [224,225] and communications of sensors [226,227,228,229,230,231], mobile [116,232], satellite [233], wireless [234] and 5G Systems [235].…”

Section: Real-world Tasks Orientatedmentioning

confidence: 99%

A Review of Designs and Applications of Echo State Networks

Sun¹,

Song²,

Hong³

et al. 2020

Preprint

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“…Wei Li et al studied a set of classification models from the angle of granular computing and achieved good results [35]- [37]. Granular computing has been widely used, such as using granular computing and reply state networks to build error diagnosis models [38], building neural networks from a granular perspective for classification [39], [40] etc.…”

Section: Introductionmentioning

confidence: 99%

Random Fuzzy Clustering Granular Hyperplane Classifier

Zhang

Chen

et al. 2020

IEEE Access

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Granular computing is a method of studying human intelligent information processing, which has advantage of knowledge discovery. In this paper, we convert a classification problem of sample space into a classification problem of fuzzy clustering granular space and propose a random fuzzy clustering granular hyperplane classifier (RFCGHC) from the perspective of granular computing. Most classifiers are only used to process numerical data, RFCGHC can process not only non-numerical data, such as information granules, but also numerical data. The classic granulation method is generally serial granulation, which has high time complexity. We design a parallel distributed granulation method to enhance efficiency. First, a clustering algorithm with adaptive cluster center number is proposed, where the ratio of standard deviation between categories and standard deviation within categories is as evaluation criterion. The clusters and the optimal amount of cluster centers can be achieved by the method. On the basis of these, sample set can be divided into many subsets and each sample can be granulated by these cluster centers. Then, a fuzzy clustering granular space can be formed, where fuzzy clustering granules, fuzzy clustering granular vectors, and their operators can be defined. In order to get the optimal hyperplane in the fuzzy clustering granular space to classify these samples, we design a loss function and evaluate each category with probability by fuzzy clustering granular hyperplane. In solving the loss function, genetic algorithm based on fuzzy clustering granules is adopted. Experimental results and theoretical analysis show that RFCGHC has good performance.

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Deep Learning Models Evolution Applied to Biomedical Engineering

Garza-Ulloa

2022

Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models

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Fault diagnosis model based on Granular Computing and Echo State Network

Cited by 11 publications

References 30 publications

A Review of Designs and Applications of Echo State Networks

A Review of Designs and Applications of Echo State Networks

Random Fuzzy Clustering Granular Hyperplane Classifier

Deep Learning Models Evolution Applied to Biomedical Engineering

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