Extreme Learning Machines with frequency based noise filtering for prediction of critical digressions in a noisy industrial process

Gupta, Aditya; Manikumar, Pln; Reddy, Ravinithesh; Bhattacharya, Arya K.

doi:10.1109/indicon.2017.8487562

Cited by 6 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CNN is mainly used to extract useful information from the data, given its strong filtering properties. As well as CNN, extreme learning machine (ELM), is becoming more and more popular to solve classification [15], regression [16], and data compression [17] problems. Unlike other algorithms, ELM provides lower training time, which makes it suitable for computationally restrained applications.…”

mentioning

confidence: 99%

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

Quezada-Gaibor

Klus

et al. 2023

J. Ind. Sea. Pos. Nav.

View full text Add to dashboard Cite

Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k-nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline.

show abstract

mentioning

confidence: 99%

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

Quezada-Gaibor

Klus

et al. 2023

J. Ind. Sea. Pos. Nav.

View full text Add to dashboard Cite

show abstract

Real Time Predictions Of Adverse Digressions In Critical and Noisy Industrial Processes Using LSTMs

et al. 2020

View full text Add to dashboard Cite

Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples

2021

IEEE Internet Things J.

View full text Add to dashboard Cite

Extreme Learning Machines with frequency based noise filtering for prediction of critical digressions in a noisy industrial process

Cited by 6 publications

References 13 publications

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

Real Time Predictions Of Adverse Digressions In Critical and Noisy Industrial Processes Using LSTMs

Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples

Contact Info

Product

Resources

About