Echo state kernel recursive least squares algorithm for machine condition prediction

Zhou, Haowen; Huang, Jinquan; Lü, Feng; Thiyagalingam, Jeyarajan; Kirubarajan, T.

doi:10.1016/j.ymssp.2018.03.047

Cited by 22 publications

(9 citation statements)

References 46 publications

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“…is the regularization factor. In order to keep the size of kernel matrix unchanged, the kernel matrix uses the new sample data to add new rows and new columns by ( 21)- (23).…”

Section: B Sliding Window Methodsmentioning

confidence: 99%

“…else calculate the maximum changeable window size M1. calculate the kernel matrix according to ( 21)- (23). when the size of the kernel matrix is restored to M1 calculate the kernel matrix according to ( 24)- (25).…”

Section: Improved Kernel Recursive Least Square Algorithmmentioning

confidence: 99%

“…From Table 1, it can be seen that compared with KAPA, KRLS has lower prediction efficiency, but it has more advantages in terms of speed and prediction accuracy. Because the KRLS algorithm has a strong tracking ability in solving nonlinear problems, it has been successfully applied in data mining, machine learning and other fields [22,23]. However, the computational complexity of KRLS will increase due to recursive calculations, so most studies mainly concentrated on the sparsification of the KRLS algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Sparse Kernel RLS Algorithm for Identification of Nonlinear Systems

Guo

Jiang

et al. 2021

IEEE Access

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“…is the regularization factor. In order to keep the size of kernel matrix unchanged, the kernel matrix uses the new sample data to add new rows and new columns by ( 21)- (23).…”

Section: B Sliding Window Methodsmentioning

confidence: 99%

Section: Improved Kernel Recursive Least Square Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Sparse Kernel RLS Algorithm for Identification of Nonlinear Systems

Guo

Jiang

et al. 2021

IEEE Access

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“…In the second part, the fusion of a Bayesian classifier is recommended as an effective solution to diagnose incipient interturn fault in the machine. To track the health status of a degraded system and predict the remaining service life of a turbofan engine, Zhou et al [87] propose a method that combines the echo state kernel recursive least-squares algorithm and a Bayesian technique, which demonstrates an excellent performance with respect to long-term prediction.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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“…Outras configurações robustas da ESN abrem mão da habitual camada de saída linear por uma não linear, buscando apreender o máximo de informação oriunda do reservatória de dinâmicas. Uma dessas abordagensé a formulação usando máquina de vetor suporte (suport vector machine) com função custo robusta, tais como a função perda -insensível ( -insensitive loss) ou a função de Huber (Shi and Han, 2007).É possível também usar regras de aprendizado não lineares, como o algoritmo dos mínimos quadrados recursivo kernelizado (kernel recursive least squares) (Zhou et al, 2018). Apesar de não terem sido avaliadas diretamente em cenários com outliers, existem trabalhos em que arquiteturas ESN treinadas com o algoritmo Laplacian eigenmaps (Han and Xu, 2018) e modelos de regressão através de processos gaussianos (Chatzis and Demiris, 2011) podem ser capazes de lidar com os efeitos adversos causados por outliers.…”

Section: Introductionunclassified

Rede De Ecos De Estados Robusta Para Identificação Recursiva De Sistemas

Bessa

Barreto

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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The use of recurrent neural networks in online system identification is very limited in real-world applications, mainly due to the propagation of errors caused by the iterative nature of the prediction task over multiple steps ahead. Bearing this in mind, we revisit design issues regarding the robustness of the echo state network (ESN) model in such online learning scenarios using a recursive estimation algorithm and an outlier robust-variant of it. By means of a comprehensive set of experiments, we show that the performance of the ESN is dependent on the adequate choice of the feedback pathways and that the prediction instability is amplified by the norm of the output weight vector, an often neglected issue in related studies. Resumo: O emprego de redes neurais recorrentes na identificação online de sistemasé bastante limitada em aplicações reais, principalmente devidoà propagação do erro causada pela natureza iterativa da tarefa de predição ao longo de vários passosà frente. Tendo isso em mente, revisitase questões de projeto relacionadasà robustez do modelo da rede de ecos de estados (echo state network, ESN) em tais cenários de aprendizado, usando um algoritmo de estimação recursiva e uma de suas variantes robusta a outlier. Por meio de um conjunto abrangente de experimentos, mostra-se que o desempenho da ESNé dependente da escolha adequada das vias de realimentação e que a instabilidade da previsãoé ampliada pela norma do vetor de peso de saída, uma questão frequentemente negligenciada em estudos relacionados.

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Echo state kernel recursive least squares algorithm for machine condition prediction

Cited by 22 publications

References 46 publications

A New Sparse Kernel RLS Algorithm for Identification of Nonlinear Systems

A New Sparse Kernel RLS Algorithm for Identification of Nonlinear Systems

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Rede De Ecos De Estados Robusta Para Identificação Recursiva De Sistemas

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