Convergence Analysis of Adaptive Exponential Functional Link Network

Patel, Vinal; Bhattacharjee, Sankha Subhra; George, Nithin V.

doi:10.1109/tnnls.2020.2979688

Cited by 18 publications

(8 citation statements)

References 37 publications

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“…This discriminative feature extraction is performed by signal processing model shown in Fig 3. [14,15]. AEFLN is deployed for various non-linear applications such as system identification, echo cancellation, noise control.…”

Section: Feature Extraction Using Psd Techniquementioning

confidence: 99%

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A Robust Feature Extraction and Deep Learning Approach for Cancer Gene Prognosis

Kumari

Seventline

2022

International Journal of Biology and Biomedical Engineering

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Mutated genes are one of the prominent factors in origination and spread of cancer disease. Here we have used Genomic signal processing methods to identify the patterns that differentiate cancer and non-cancerous genes. Furthermore, Deep learning algorithms were used to model a system that automatically predicts the cancer gene. Unlike the existing methods, two feature extraction modules are deployed to extract six attributes. Power Spectral Density based module was used to extract statistical parameters like Mean, Median, Standard deviation, Mean Deviation and Median Deviation. Adaptive Functional Link Network (AFLN) based filter module was used to extract Normalized Mean Square Error (NMSE). The uniqueness of this paper is identification of six input features that differentiates cancer genes. In this work artificial neural network is developed to predict cancer genes. Comparison is done on three sets of datasets with 6 attributes, 5 attributes and one attribute. We performed all the training and testing on the Tensorflow using the Keras library in Python using Google Colab. The developed approach proved its efficiency with 6 attributes attaining an accuracy of 98% for 150 epochs. The ANN model was also compared with existing work and attained a 10 fold cross validation accuracy of 96.26% with an increase of 1.2%.

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Section: Feature Extraction Using Psd Techniquementioning

confidence: 99%

“…Feature extraction block using PSD technique 2. Feature Extraction using AFLN AFLN model is a modified version of Adaptive exponential Functional Link Network (AEFLN) proposed by[14,15]. AEFLN is deployed for various non-linear applications such as system identification, echo cancellation, noise control.…”

mentioning

confidence: 99%

A Robust Feature Extraction and Deep Learning Approach for Cancer Gene Prognosis

Kumari

Seventline

2022

International Journal of Biology and Biomedical Engineering

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“…Assumption 3: The error sequence e(n) is asymptotically uncorrelated with g(n) 2 and |h T w(n)| 2 . This has been commonly used for analyzing EFLN-based algorithms at steady-state [42,46].…”

Section: Steady-state Performancementioning

confidence: 99%

Study of Frequency domain exponential functional link network filters

Yu¹,

Tana²,

Lamareb³

et al. 2022

Preprint

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The exponential functional link network (EFLN) filter has attracted tremendous interest due to its enhanced nonlinear modeling capability. However, the computational complexity will dramatically increase with the dimension growth of the EFLN-based filter. To improve the computational efficiency, we propose a novel frequency domain exponential functional link network (FDEFLN) filter in this paper. The idea is to organize the samples in blocks of expanded input data, transform them from time domain to frequency domain, and thus execute the filtering and adaptation procedures in frequency domain with the overlap-save method. A FDEFLN-based nonlinear active noise control (NANC) system has also been developed to form the frequency domain exponential filtered-s least mean-square (FDEFsLMS) algorithm. Moreover, the stability, steady-state performance and computational complexity of algorithms are analyzed. Finally, several numerical experiments corroborate the proposed FDEFLN-based algorithms in nonlinear system identification, acoustic echo cancellation and NANC implementations, which demonstrate much better computational efficiency.

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“…By IPLNAs in this paper, we consider a wide family of shallow neural networks including the extreme learning machine (ELM) or random vector functional link (RVFL) networks [1]- [3], other functional links and kernel neural architectures and filters, e.g., [4]- [6], and polynomial neural networks [7], [8] including basic standalone  structures called polynomial neural units in [9], [10], and references therein. This letter shows that the input-to-state stability (ISS) concept [11] and BIBS stability [12] generally apply to the gradient learning algorithms and their many modifications for IPLNA's.…”

Section: Introductionmentioning

confidence: 99%

Letter on Convergence of In-Parameter-Linear Nonlinear Neural Architectures With Gradient Learnings

Bukovský

Dohnal

Beneš

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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This letter summarizes and proves the concept of bounded-input bounded-state (BIBS) stability for weight convergence of a broad family of in-parameter-linear nonlinear neural architectures as it generally applies to a broad family of incremental gradient learning algorithms. A practical BIBS convergence condition results from the derived proofs for every individual learning point or batches for real-time applications.

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Convergence Analysis of Adaptive Exponential Functional Link Network

Cited by 18 publications

References 37 publications

A Robust Feature Extraction and Deep Learning Approach for Cancer Gene Prognosis

A Robust Feature Extraction and Deep Learning Approach for Cancer Gene Prognosis

Study of Frequency domain exponential functional link network filters

Letter on Convergence of In-Parameter-Linear Nonlinear Neural Architectures With Gradient Learnings

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