Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification
Pengelasan corak adalah salah satu isu utama dalam pelbagai tugas pencarian data. Dalam kajian ini, fokus penyelidikan tertumpu kepada reka bentuk dan pembinaan model hibrid yang menggabungkan rangkaian neural Teori Resonan Adaptif (ART) terselia dan model Pembelajaran Pengukuhan (RL) untuk penge...
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my-usm-ep.410312018-08-16T06:49:51Z Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification 2015-10 Navan, Farhad Pourpanah T Technology TK7800-8360 Electronics Pengelasan corak adalah salah satu isu utama dalam pelbagai tugas pencarian data. Dalam kajian ini, fokus penyelidikan tertumpu kepada reka bentuk dan pembinaan model hibrid yang menggabungkan rangkaian neural Teori Resonan Adaptif (ART) terselia dan model Pembelajaran Pengukuhan (RL) untuk pengelasan corak. Secara khususnya, rangkaian ARTMAP Kabur (FAM) dan Pembelajaran-Q dijadikan sebagai tulang belakang dalam merekabentuk dan membina model-model hibrid. Satu model QFAM baharu terlebih dahulu diperkenalkan bagi menambahbaik prestasi pengelasan rangkaian FAM. Strategi pruning dimasukkan bagi mengurangkan kekompleksan QFAM. Bagi mengatasi isu ketidak-telusan, Algoritma Genetik (GA) digunakan bagi mengekstrak hukum kabur if-then daripada QFAM. Model yang terhasil iaitu QFAM-GA, dapat memberi ramalan berserta dengan huraian dengan hanya menggunakan bilangan antisiden yang sedikit. Bagi menambahkan lagi kebolehtahanan model-model Q-FAM, penggunaan sistem agenpelbagai telah dicadangkan. Hasilnya, model gugusan QFAM berasaskan agen dengan ukuran percaya dan kaedah rundingan baharu telah dicadangkan. Pelbagai jenis masalah tanda-aras telah digunakan bagi penilaian model-model gugusan dan individu berasaskan QFAM. Hasilnya telah dianalisa dan dibandingkan dengan FAM serta model-model yang dilaporkan dalam kajian terdahulu. Sebagai tambahan, dua daripada masalah dunia-nyata digunakan bagi menunjukkan kebolehan praktikal model hibrid. Keputusan akhir menunjukkan keberkesanan modul berasaskan QFAM dalam menerajui tugas-tugas pengelasan corak. ________________________________________________________________________________________________________________________ Pattern classification is one of the primary issues in various data mining tasks. In this study, the main research focus is on the design and development of hybrid models, combining the supervised Adaptive Resonance Theory (ART) neural network and Reinforcement Learning (RL) models for pattern classification. Specifically, the Fuzzy ARTMAP (FAM) network and Q-learning are adopted as the backbone for designing and developing the hybrid models. A new QFAM model is first introduced to improve the classification performance of FAM network. A pruning strategy is incorporated to reduce the complexity of QFAM. To overcome the opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then rules from QFAM. The resulting model, i.e. QFAM-GA, is able to provide predictions with explanations using only a few antecedents. To further improve the robustness of QFAM-based models, the notion of multi agent systems is employed. As a result, an agent-based QFAM ensemble model with a new trust measurement and negotiation method is proposed. A variety of benchmark problems are used for evaluation of individual and ensemble QFAM-based models. The results are analyzed and compared with those from FAM as well as other models reported in the literature. In addition, two real-world problems are used to demonstrate the practicality of the hybrid models. The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks. 2015-10 Thesis http://eprints.usm.my/41031/ http://eprints.usm.my/41031/1/Hybrid_Models_Of_Fuzzy_Artmap_And_Qlearning_For_Pattern_Classification.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik Dan Elektronik |
| institution |
Universiti Sains Malaysia |
| collection |
USM Institutional Repository |
| language |
English |
| topic |
T Technology TK7800-8360 Electronics |
| spellingShingle |
T Technology TK7800-8360 Electronics Navan, Farhad Pourpanah Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification |
| description |
Pengelasan corak adalah salah satu isu utama dalam pelbagai tugas pencarian
data. Dalam kajian ini, fokus penyelidikan tertumpu kepada reka bentuk dan
pembinaan model hibrid yang menggabungkan rangkaian neural Teori Resonan
Adaptif (ART) terselia dan model Pembelajaran Pengukuhan (RL) untuk pengelasan
corak. Secara khususnya, rangkaian ARTMAP Kabur (FAM) dan Pembelajaran-Q
dijadikan sebagai tulang belakang dalam merekabentuk dan membina model-model
hibrid. Satu model QFAM baharu terlebih dahulu diperkenalkan bagi menambahbaik
prestasi pengelasan rangkaian FAM. Strategi pruning dimasukkan bagi
mengurangkan kekompleksan QFAM. Bagi mengatasi isu ketidak-telusan, Algoritma
Genetik (GA) digunakan bagi mengekstrak hukum kabur if-then daripada QFAM.
Model yang terhasil iaitu QFAM-GA, dapat memberi ramalan berserta dengan
huraian dengan hanya menggunakan bilangan antisiden yang sedikit. Bagi
menambahkan lagi kebolehtahanan model-model Q-FAM, penggunaan sistem agenpelbagai
telah dicadangkan. Hasilnya, model gugusan QFAM berasaskan agen
dengan ukuran percaya dan kaedah rundingan baharu telah dicadangkan. Pelbagai
jenis masalah tanda-aras telah digunakan bagi penilaian model-model gugusan dan
individu berasaskan QFAM. Hasilnya telah dianalisa dan dibandingkan dengan FAM
serta model-model yang dilaporkan dalam kajian terdahulu. Sebagai tambahan, dua
daripada masalah dunia-nyata digunakan bagi menunjukkan kebolehan praktikal
model hibrid. Keputusan akhir menunjukkan keberkesanan modul berasaskan QFAM
dalam menerajui tugas-tugas pengelasan corak.
________________________________________________________________________________________________________________________
Pattern classification is one of the primary issues in various data mining
tasks. In this study, the main research focus is on the design and
development of hybrid models, combining the supervised Adaptive
Resonance Theory (ART) neural network and Reinforcement Learning (RL)
models for pattern classification. Specifically, the Fuzzy ARTMAP (FAM)
network and Q-learning are adopted as the backbone for designing and
developing the hybrid models. A new QFAM model is first introduced to
improve the classification performance of FAM network. A pruning strategy
is incorporated to reduce the complexity of QFAM. To overcome the
opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then
rules from QFAM. The resulting model, i.e. QFAM-GA, is able to provide
predictions with explanations using only a few antecedents. To further
improve the robustness of QFAM-based models, the notion of multi agent
systems is employed. As a result, an agent-based QFAM ensemble model
with a new trust measurement and negotiation method is proposed. A variety
of benchmark problems are used for evaluation of individual and ensemble
QFAM-based models. The results are analyzed and compared with those
from FAM as well as other models reported in the literature. In addition, two
real-world problems are used to demonstrate the practicality of the hybrid
models. The outcomes indicate the effectiveness of QFAM-based models in
tackling pattern classification tasks.
|
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Navan, Farhad Pourpanah |
| author_facet |
Navan, Farhad Pourpanah |
| author_sort |
Navan, Farhad Pourpanah |
| title |
Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification |
| title_short |
Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification |
| title_full |
Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification |
| title_fullStr |
Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification |
| title_full_unstemmed |
Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification |
| title_sort |
hybrid models of fuzzy artmap and qlearning for pattern classification |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Kejuruteraan Elektrik Dan Elektronik |
| publishDate |
2015 |
| url |
http://eprints.usm.my/41031/1/Hybrid_Models_Of_Fuzzy_Artmap_And_Qlearning_For_Pattern_Classification.pdf |
| _version_ |
1747820864830701568 |