Advance Predictions of critical digressions in a noisy industrial process- performance of Extreme Learning Machines versus Artificial Neural Networks

Reddy, Ravinithesh; Bhattacharya, Arya K.; Rishita, G.

doi:10.1016/j.ifacol.2018.05.017

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…First, while trying to improve global optimization, the local best may be ignored. Second, the accuracy of an algorithm is improved by the price of diminishing processing speed (Reddy et al 2018). Third, if there are outliers in the training samples, the hidden layer output matrix is ill-posed, which will affect the generalization ability and robustness of the model (Zhang & Zhang 2016).…”

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confidence: 99%

A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

Chen

Zhang

Karimian

et al. 2021

Journal of Hydroinformatics

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Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions.

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confidence: 99%