An incremental adaptive neural network model for online noisy data regression and its application to compartment fire studies

Lee, Eric Wai Ming

doi:10.1016/j.asoc.2010.01.002

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…5. When a change is detected with respect to the old patterns, the first step is to calculate the values a for the new data point according to Equation (5). Then, among all the FVs in the model with non-zero values in a, the one with least contribution, say m I , is deleted from the model using Decremental Learning as in [29] and m I is reset to zero.…”

Section: Discussionmentioning

confidence: 99%

“…Different approaches have been developed for tackling the different pattern drifts problems, which can be categorized into adaptive single model [5][6][7] and online learning ensembles [8][9][10]. The former approach is based on an adaptive model that learns incrementally the new patterns and/or forgets the old inefficient ones; however in practice, the computational burden for incremental learning is unacceptable for large datasets, and the recurring patterns are not efficiently handled if they have already been deleted from the model.…”

Section: Q4mentioning

confidence: 99%

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A SVR-based ensemble approach for drifting data streams with recurring patterns

Liu

Zio

2016

Applied Soft Computing

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Pattern drift is a common issue for machine learning in real applications, as the distribution generating the data may change under nonstationary environmental/operational conditions. In our previous work, a strategy based on Feature Vector Selection (FVS) has been proposed for enabling a Support Vector Regression (SVR) model to adaptively update with streaming data, but the proposed strategy suffers from the incapability of treating recurring patterns. An instance-based online learning approach is proposed in this paper, which can adaptively update an SVR-based ensemble model with steaming data points. The proposed approach reduces the computational complexity of the updating process by selecting only part of the newly available data and allows following timely the ongoing patterns by resorting to FVS. The proposed approach creates new sub-models directly from a basic model and the sub-models represent separately the data stream at different periods. A dynamic ensemble selection strategy is integrated in the approach to select the sub-models most relevant to the new data point for deriving the prediction, while reducing the influence of the irrelevant ones. The weights of the different models in the ensemble are updated, based on their prediction errors. Comparison results with several benchmark approaches on several synthetic datasets and on the dataset concerning the leakage from the first seal in a Reactor Coolant Pump, prove the efficiency and accuracy of the proposed online learning ensemble approach

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Section: Discussionmentioning

confidence: 99%

Section: Q4mentioning

confidence: 99%

A SVR-based ensemble approach for drifting data streams with recurring patterns

Liu

Zio

2016

Applied Soft Computing

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“…Image recognition techniques could be a useful technology to solve this problem. Another approach to predict the location of the thermal interface in a fire compartment (HTI) is studied in the work of Lee et al [55]. A neural network model based on probabilistic entropy (PENN) was used as an alternative to the CFD technique (computer simulation).…”

Section: Flashover Occurrence Predictionmentioning

confidence: 99%

A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

et al. 2020

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Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, The difficulty of handling this complicated emergency through fire services can have fatal consequences for their staff. Therefore, there is a huge demand for new methods and technologies to tackle this life-threatening emergency. Modelling and simulation techniques have been adopted to conduct research due to the complexity of obtaining a real cases database related to this phenomenon. In this paper, a review of the literature related to the modelling and simulation of enclosure fires with respect to the flashover phenomenon is carried out. Furthermore, the related literature for comparing images from thermal cameras with computed images is reviewed. Finally, the suitability of artificial intelligence (AI) techniques for flashover prediction in enclosed spaces is also surveyed.

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“…We adopted the noise-corrupted sine curve benchmarking problem (Lee, 2011) As both the PENN and AKNN models are online training models, their performances are sensitive to the order in which samples are presented in the model training. Therefore, we further investigated their performances using the statistical approach as follows.…”

Section: Noise-corrupted Sine Curvementioning

confidence: 99%

A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction

et al. 2013

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Building cooling load prediction is critical to the success of energy-saving measures.While many of the computational models currently available in the industry have been developed for this purpose, most require extensive computer resources and involve lengthy computational processes. Artificial neural networks (ANNs) have recently been adopted for prediction, and pioneering works have confirmed the feasibility of this approach. However, users are required to predetermine an ANN model's parameters.This hinders the applicability of the ANN approach in actual engineering problems, as most engineers may be unfamiliar with soft computing. This paper proposes a fully autonomous kernel-based neural network (AKNN) model for noisy data regression prediction. No part of the model's mechanism requires human intervention; rather, it self-organises its structure according to the training samples presented. Unlike the other existing autonomous models, the AKNN model is an online learning model. It is particularly suitable for online steps-ahead prediction. In this paper, we benchmark the AKNN model's performance according to other ANN models. It is also successfully E.W.M. Lee () • I.

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An incremental adaptive neural network model for online noisy data regression and its application to compartment fire studies

Cited by 14 publications

References 44 publications

A SVR-based ensemble approach for drifting data streams with recurring patterns

A SVR-based ensemble approach for drifting data streams with recurring patterns

A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction

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