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Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method and Modified Cuckoo Search

ISSN : 0975-900X
Journal from gdlhub / 2017-08-14 11:52:34
Oleh : Hamid Salimi1, Davar Giveki1,2, Mohammad Ali Soltanshahi1, Javad Hatami1, International Journal of Artificial Intelligence & Applications
Dibuat : 2012-07-02, dengan 1 file

Keyword : Back Propagation (BP) algorithm, Gradient Decent (GD), Conjugate Gradient (CG), Modified Cuckoo Search (MCS), Mixture of Experts (MEs)
Subjek : Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method and Modified Cuckoo Search
Url : http://airccse.org/journal/ijaia/papers/3112ijaia01.pdf
Sumber pengambilan dokumen : Internet

This paper investigates a new method for improving the learning algorithm of Mixture of Experts (ME)


model using a hybrid of Modified Cuckoo Search (MCS) and Conjugate Gradient (CG) as a second order


optimization technique. The CG technique is combined with Back-Propagation (BP) algorithm to yield a


much more efficient learning algorithm for ME structure. In addition, the experts and gating networks in


enhanced model are replaced by CG based Multi-Layer Perceptrons (MLPs) to provide faster and more


accurate learning. The CG is considerably depends on initial weights of connections of Artificial Neural


Network (ANN), so, a metaheuristic algorithm, the so-called Modified Cuckoo Search is applied in order to


select the optimal weights. The performance of proposed method is compared with Gradient Decent Based


ME (GDME) and Conjugate Gradient Based ME (CGME) in classification and regression problems. The


experimental results show that hybrid MSC and CG based ME (MCS-CGME) has faster convergence and


better performance in utilized benchmark data sets.

Deskripsi Alternatif :

This paper investigates a new method for improving the learning algorithm of Mixture of Experts (ME)


model using a hybrid of Modified Cuckoo Search (MCS) and Conjugate Gradient (CG) as a second order


optimization technique. The CG technique is combined with Back-Propagation (BP) algorithm to yield a


much more efficient learning algorithm for ME structure. In addition, the experts and gating networks in


enhanced model are replaced by CG based Multi-Layer Perceptrons (MLPs) to provide faster and more


accurate learning. The CG is considerably depends on initial weights of connections of Artificial Neural


Network (ANN), so, a metaheuristic algorithm, the so-called Modified Cuckoo Search is applied in order to


select the optimal weights. The performance of proposed method is compared with Gradient Decent Based


ME (GDME) and Conjugate Gradient Based ME (CGME) in classification and regression problems. The


experimental results show that hybrid MSC and CG based ME (MCS-CGME) has faster convergence and


better performance in utilized benchmark data sets.

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