Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes

Randriamamonjy Liantsoa Joharinirina; Randimbindrainibe Falimanana; Robinson Matio

Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes

February 2019

Vol-5, Issue-1

Paper ID: 9494

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Sciences and Applications

Keywords

Extreme learning machine (ELM) Growing algorithm Incremental learning Moore-Penrose Generalized Inverse Sequential learning minimizing error.

Abstract

One of the open problems in neural network research is how to automatically determine network architectures for given application. The extreme learning machine (ELM) have been proposed for generalized single-hidden-layer feedforward networks (SLFNs) which perform well in both regression and classification application. In this paper, an error-minimized incremental algorithm based on Sequential Moore-Penrose Inverse is proposed, to automatically determine the number of hidden nodes in SLFNs. This approach, Sequential Moore-Penrose inverse based ELM (SMP-ELM), is able to add random hidden nodes to SLFNs one by one or group by group. During the growth of the networks, the output weights are updated incrementally. Simulation results demonstrate and verify that our new approach can achieve more compact network structure with better generalization performance.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Randriamamonjy Liantsoa Joharinirina	University of Antananarivo, Madagascar
2	Randimbindrainibe Falimanana	University of Antananarivo, Madagascar
3	Robinson Matio	University of Antananarivo, Madagascar

How to Cite

Use the following formats to cite this article in your research.

APA Style

Joharinirina, Randriamamonjy Liantsoa, Falimanana, Randimbindrainibe, & Matio, Robinson (2019). Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes. International Journal of Advance Research and Innovative Ideas In Education, 5(1), 666-672.

MLA Style

Joharinirina, Randriamamonjy Liantsoa, et al. "Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes." International Journal of Advance Research and Innovative Ideas In Education, vol. 5, no. 1, 2019, pp. 666-672.

IEEE Style

Randriamamonjy Liantsoa Joharinirina, Randimbindrainibe Falimanana, and Robinson Matio, "Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes," International Journal of Advance Research and Innovative Ideas In Education, vol. 5, no. 1, pp. 666-672, 2019.

Vancouver Style

Joharinirina Randriamamonjy Liantsoa, Falimanana Randimbindrainibe, Matio Robinson. Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes. International Journal of Advance Research and Innovative Ideas In Education. 2019;5(1):666-672.

Harvard Style

Joharinirina, Randriamamonjy Liantsoa, Falimanana, Randimbindrainibe, & Matio, Robinson (2019) 'Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes', International Journal of Advance Research and Innovative Ideas In Education, 5(1), pp. 666-672.

Chicago Style

Joharinirina, Randriamamonjy Liantsoa, Randimbindrainibe Falimanana, and Robinson Matio. "Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes." International Journal of Advance Research and Innovative Ideas In Education 5, no. 1 (2019): 666-672.

Turabian Style

Joharinirina, Randriamamonjy Liantsoa, Randimbindrainibe Falimanana, and Robinson Matio. "Sequential Moore-Penrose Inverse Based Extreme Learning Machine with Growth of Hidden Nodes." International Journal of Advance Research and Innovative Ideas In Education 5, no. 1 (2019): 666-672.