Supervised Learning for Neural Network Using Ant Colony Optimization

被引：0

作者：

Rathee, Ravinder ^{[1
]}

Rani, Seema ^{[2
]}

Dagar, Anita ^{[3
]}

机构：

[1] CR Polytech, Rohtak, Haryana, India

[2] Dravidian Univ, Agaram, AP, India

[3] MRIU, Dept Comp Sci & Engn, Faridabad, Haryana, India

来源：

PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON RELIABILTY, OPTIMIZATION, & INFORMATION TECHNOLOGY (ICROIT 2014) | 2014年

关键词：

Ant colony; supervised learning; SLNNA; Neural Network;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

To describe the approach of real-world activities we have proposed an idea of SLNA algorithm and its diagram. In this paper we are using supervised learning to train the network. In supervised learning desire response is provided by the teacher in correspondence to the particular input. To explain the concept of SLNNA algorithm we have used a real-world example of travel agency (make my trip agency). To optimize the path in the search space, we have used ATSP algorithm.

引用

页码：331 / 334

页数：4

共 13 条

[1]

Alba E, 2004, LECT NOTES COMPUT SC, V3102, P852

[2]

Blum C., 2005, 5 INT C HYBRID INTEL, P233

[3]

Conforth Matthew, 2008 IEEE SWARM INT

[4] Ant system: Optimization by a colony of cooperating agents [J].

Dorigo, M ;

Maniezzo, V ;

Colorni, A .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1996, 26 (01) :29-41

[5]

Dorigo M., 2007, Scholarpedia, V2, P14, DOI DOI 10.4249/scholarpedia.1461

[6]

Eric W., 2008, 19 INT S SOFTW REL E

[7]

Gudise Venu G., COMP PARTICLE SWARM

[8]

Haza Nuzley A., 2008, JURNAL TEKNOLOGI, V49, P13

[9]

Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968

[10]

Pillai K. G, 2008, SWARM INT SIS 2011 I

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