Nonparametric fuzzy regression—k-NN and kernel smoothing techniques

Ченг, Чиа-Чи; Lee, E.S.

doi:10.1016/s0898-1221(99)00198-4

Cited by 49 publications

(32 citation statements)

References 9 publications

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“…The development of the local model is based on the Fuzzy k-Nearest Neighbors (F-kNN) approach. Compared to other techniques, F-kNN is simple, easily interpretable and can achieve an acceptable accuracy rate [17], [18], [19]. The fuzzy version of k-NN averages the value of the points closest to the query point, on the assumption that points close to each other have similar values [20].…”

Section: Hybrid Incremental Modelingmentioning

confidence: 99%

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Haber¹,

Toro²

2017

Preprint

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Abstract-A novel method based on a hybrid incremental modeling approach has been designed and applied to imbalance detection in ultra-high precision rotating machines. The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearestneighbour) to take advantage of their complementary capacities. Three normalization strategies of evaluating the effect on accuracy are analyzed. A comparative study demonstrates that the hybrid incremental model provides better error-based performance indices for detecting imbalance than a nonlinear regression model and an adaptive neural-fuzzy inference system model. The suitability of Mahanolobis normalization for hybrid incremental modeling is also demonstrated in this case study. The proposed strategy for imbalance detection is simple, fast, and non-intrusive, reducing the deterioration in the performance of ultra-high precision rotating machines due to vibrations.Index Terms-modeling, intelligent systems, imbalance detection, ultra-high precision rotating machine.

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Section: Hybrid Incremental Modelingmentioning

confidence: 99%

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Haber¹,

Toro²

2017

Preprint

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“…Compared to other techniques, F -feNN is simple, easily interpretable and can achieve an acceptable accuracy rate [14], [19], [20]. The fuzzy version of fe-NN averages the value of the points closest to the query point, on the assumption that points close to each other have similar values [21].…”

Section: B Local Modelmentioning

confidence: 99%

“…Since the basic or global level has to be as simple as possible, linear regression techniques are clearly a viable solution because of their straightforward application. The counterpart technique we selected for the subsequent refinement of the basic model in the form of hybrid incremental models is the fuzzy fc-nearest-neighbors smoothing algorithm [14].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes

Penedo

Haber

Gajate

et al. 2012

IEEE Trans. Ind. Inf.

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Abstract-There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy fe-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

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“…It shows the crisp covariate has the influence on the early warning of macro-economic index Y . Now, we compare the given methods in this paper with the k-NN and kernel smoothing techniques in nonparametric fuzzy regression (Cheng and Lee 1999). Similar to Cheng and Lee (1999), the k-NN smoother is defined as…”

Section: An Illustrative Examplementioning

confidence: 99%

Fuzzy Statistical Analysis of Multiple Regression with Crisp and Fuzzy Covariates and Applications in Analyzing Economic Data of China

2010

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Nonparametric fuzzy regression—k-NN and kernel smoothing techniques

Cited by 49 publications

References 9 publications

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes

Fuzzy Statistical Analysis of Multiple Regression with Crisp and Fuzzy Covariates and Applications in Analyzing Economic Data of China

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