Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks

Abusnaina, Ahmed A. A. (2015) Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks. ["eprint_fieldopt_thesis_type_phd" not defined] thesis, Universiti Sains Malaysia.

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Abstract

Membangunkan kaedah latihan yang cekap untuk Rangkaian Neural (NN) dalam mencapai kejituan yang tinggi adalah satu cabaran. Tambahan pula, latihan NN masih lagi memerlukan masa yang lama. Algoritma Pengoptimuman Perayauan Kupang (MWO) ialah satu algoritma pengoptimuman metaheuristik yang baru dan telah diinspirasikan secara ekologi oleh tingkah laku pegerakan kupang. Objektif utama bagi tesis ini adalah untuk mencapai prestasi yang terbaik dalam penumpuan masa latihan dan ketepatan pengelasan untuk pengelasan corak dengan mengusulkan kaedah latihan penyeliaan yang baru untuk Rangkaian Neural Buatan (ANN) yang berasaskan penggunaan algoritma MWO. Mempertingkatkan prestasi, terutamanya dalam kejituan pengelasan yang membawa kepada perkenalan versi MWO yang telah di adaptasi; dikenali sebagai algoritma Peningkatan-MWO (E-MWO). Developing efficient training method for Neural Networks (NN) in terms of high accuracy is a challenge. In addition, training NN is still highly-time consuming. The Mussels Wandering Optimization (MWO) is a recent metaheuristic optimization algorithm inspired ecologically by mussels movement behavior. The major objective of this thesis is to achieve better performance in terms of convergence training time and classification accuracy for pattern classification by proposing new supervised training methods for Artificial Neural Networks (ANN) based on the MWO algorithm. Increasing the performance, especially in terms of classification accuracy led to an adapted version of the MWO; known as Enhanced-MWO (E-MWO) algorithm.

Item Type: Thesis (["eprint_fieldopt_thesis_type_phd" not defined])
Additional Information: Please contact ‪Ahmad Abusnaina‬ ‪‬ <abusnaina@ymail.com> for full text content.
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Pusat Pengajian Sains Komputer (School of Computer Sciences)
Depositing User: Mr Noorazilan Noordin
Date Deposited: 13 Jul 2017 03:54
Last Modified: 17 May 2018 03:12
URI: http://eprints.usm.my/id/eprint/35580

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