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Neural Processing Letters

Erschienen in:

06.11.2017

A Multi-Valued Neuron Based Complex ELM Neural Network

verfasst von: Francesco Grasso, Antonio Luchetta, Stefano Manetti

Erschienen in: Neural Processing Letters | Ausgabe 1/2018

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Abstract

In this paper, a new efficient model of neural network is proposed, which is realized by the combination of two recent and successful neurocomputing paradigms. The idea behind the work is to realize a neural network constituted by multi-valued complex neurons, which are trained with the principles of extreme learning machine (ELM). The specific kind of used neuron allows a very straightforward derivation of the ELM, with no substantial modification in the weight adjustment procedure. The main advantages that clearly emerge by this model are represented by the further increasing of generalization performances in presence of noise, together with a generalized reduction of needed nodes (neurons) in the hidden layer. The effectiveness of the proposed model will be shown by some benchmark results, also compared with the original techniques.

Vorheriger Artikel A Deep Neural Network Based on ELM for Semi-supervised Learning of Image Classification

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Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRef

Hirose A (2012) Complex-valued neural networks. Springer, BerlinCrossRefMATH

Aizenberg I (2011) Complex-valued neural networks with multi-valued neurons. Springer, HeidelbergCrossRefMATH

Aizenberg I, Paliy D, Zurada J, Astola J (2008) Blur identification by multilayer neural network based on multivalued neurons. IEEE Trans Neural Netw 19(5):883–898CrossRef

Aizenberg I (2014) MLMVN with soft margins learning. IEEE Trans Neural Netw 25(9):1632–1644CrossRef

Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61(1):85–117CrossRef

Gerstner W, Kistler WM (2002) Spiking neuron models: single neurons, populations, plasticity. Cambridge University Press, CambridgeCrossRefMATH

Yu Q, Tang H, Chen Tan K, Li H (2013) Rapid feedforward computation by temporal encoding and learning with spiking neurons. IEEE Trans Neural Netw Learn Syst 24(10):1539–1552CrossRef

Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48CrossRefMATH

10.

Chen L, Zhou L-F, Pung H-K (2008) Universal approximation and QoS violation application of extreme learning machine. Neural Process Lett 28:81–95CrossRef

11.

Zhang L, Zhang D (2016) Robust visual knowledge transfer via extreme learning machine-based domain adaptation. IEEE Trans Image Process 25(10):4959–4973MathSciNetCrossRef

12.

ZhaoH Guo X, Wang M, Li T, Pang C, Georgakopoulos D (2015) Analyze EEG signals with extreme learning machine based on PMIS feature selection. J Mach Learn Cyber Int. https://doi.org/10.1007/s13042-015-0378-x

13.

Hirose A, Aizenberg I, Mandic DP (2014) Guest editorial special issue on complex- and hypercomplex-valued neural networks. IEEE Trans Neural Netw Learn Syst 25(9):1597–1599CrossRef

14.

Hong X, Chen S, Harris CJ, Khalaf EF (2014) Single-carrier frequency domain equalization for hammerstein communication systems using complex-valued neural networks. IEEE Trans Image Process 62(17):4467–4478MathSciNetCrossRef

15.

Saad Saoud L, Rahmoune F, Tourtchine V, Baddari K (2016) Fully Complex valued wavelet network for forecasting the global solar irradiation. Neural Process Lett. https://doi.org/10.1007/s11063-016-9537-7

16.

Aizenberg I, Luchetta A, Manetti S, Piccirilli M C (2016) System identification using FRA and a modified MLMVN with arbitrary complex-valued inputs. In: Proceedings of IEEE-IJCNN international joint conference on neural networks, pp 4404–4411

17.

Savitha R, Suresh S, Sundararajan N (2012) Fast learning circular complex-valued extreme learning machine (CC-ELM) for real-valued classification problems. Inf Sci. https://doi.org/10.1016/j.ins.2011.11.003 MathSciNet

18.

Shukla S, Yadav RN (2015) Regularized weighted circular complex-valued extreme learning machine for imbalanced learning. IEEE Access. https://doi.org/10.1109/ACCESS.2015.2506601

19.

Aizenberg I, Moraga C, Paliy D (2005) A feedforward neural network based on multi-valued neurons. advances in soft computing, XIV. In: Reusch B (ed) Computational intelligence, theory and applications. Springer, Berlin, pp 599–612CrossRef

20.

Aizenberg I, Luchetta A, Manetti S (2012) A modified learning algorithm for the multilayer neural network with multi-valued neurons based on the complex QR decomposition. Soft Comput 16(4):563–575CrossRef

21.

Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55–67CrossRefMATH

22.

Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cyber. https://doi.org/10.1007/s13042-011-0019-y

23.

Frank A, Asuncion A (2010) UCI machine learning repository. [Online]. http://archive.ics.uci.edu/ml

24.

Hirst JD, King RD, Sternberg MJE (1994) Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines. J Comput Aided Mol Des 8(4):405–420CrossRef

25.

Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529CrossRef

26.

Amato U, Masiello G, Serio C, Viggiano M (2002) The \(\sigma \)-IASI code for calculation of infrared atmosphere radiance and its derivatives. Environ Modell Softw 17:651–667CrossRef

Titel: A Multi-Valued Neuron Based Complex ELM Neural Network
verfasst von: Francesco Grasso
Antonio Luchetta
Stefano Manetti
Publikationsdatum: 06.11.2017
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 1/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-017-9745-9

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