Sains Malaysiana 43(11)(2014): 1781–1790
Multivariate
Relationship Modeling using Nested Fuzzy Cognitive Map
(Model Hubungan Multivariasi Menggunakan Peta Kognitif Kabur
Tersarang)
O. MOTLAGH1*, E.I. PAPAGEORGIOU2, S.H. TANG3 & ZAMBERI JAMALUDIN1
1Robotics and Atomation,
Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka
Malaysia
2Informatics and
Computer Tech., Technological Education Institute (TEI), Lamia, 654 04 Kavala
Greece
3Mechanical and
Manufacturing Department, Universiti Putra Malaysia, 43400 Serdang, Selangor
Malaysia
Diserahkan: 7 Jun 2013/Diterima: 11 Mac 2014
ABSTRACT
Soft computing is an alternative to hard and classic math models
especially when it comes to uncertain and incomplete data. This includes
regression and relationship modeling of highly interrelated variables with
applications in curve fitting, interpolation, classification, supervised
learning, generalization, unsupervised learning and forecast. Fuzzy cognitive
map (FCM)
is a recurrent neural structure that encompasses all possible connections
including relationships among inputs, inputs to outputs and feedbacks. This
article examines a new methods for nonlinear multivariate regression using
fuzzy cognitive map. The main contribution is the application of nested FCM structure
to define edge weights in form of meaningful functions rather than crisp
values. There are example cases in this article which serve as a platform to
modelling even more complex engineering systems. The obtained results, analysis
and comparison with similar techniques are included to show the robustness and
accuracy of the developed method in multivariate regression, along with future
lines of research.
Keywords: Nested fuzzy cognitive map; neural activation;
regression
ABSTRAK
Pengiraan lembut adalah alternatif kepada model matematik klasik
dan sukar terutama apabila ia melibatkan data yang tidak menentu dan tidak
lengkap. Ini termasuk regresi dan pemodelan hubungan pemboleh ubah yang sangat
berkait dengan aplikasi dalam penyesuaian lengkung, interpolasi, pengelasan,
pembelajaran yang diselia, generalisasi, pembelajaran tanpa penyeliaan dan
ramalan. Peta kognitif kabur (FCM) merupakan struktur neural berulang yang
merangkumi semua kemungkinan sambungan termasuk hubungan antara input, input
kepada output dan maklum balas. Artikel ini mengkaji kaedah baru untuk regresi
multivariasi tak linear menggunakan peta kognitif kabur. Penyumbang utama
adalah penggunaan struktur FCM bersarang untuk menentukan kelebihan
pemberat dalam bentuk fungsi bermakna dan bukannya nilai-nilai bersih. Terdapat
kes-kes contoh dalam artikel ini yang berfungsi sebagai satu platform untuk
pemodelan sistem kejuruteraan yang lebih kompleks. Keputusan yang diperoleh,
analisis dan perbandingan dengan teknik yang sama disertakan untuk menunjukkan
keberkesanan dan ketepatan kaedah yang dibangunkan dalam regresi multivariasi
bersama-sama dengan hala tuju untuk penyelidikan yang akan datang.
Kata kunci: pengaktifan neural; peta kognitif
kabur tersarang; regresi
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*Pengarang
untuk surat-menyurat; email: omid@utem.edu.my
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