A Novel FLANN with a Hybrid PSO and GA Based Gradient Descent Learning for Classification

被引：13

作者：

Naik, Bighnaraj ^{[1
]}

Nayak, Janmenjoy ^{[1
]}

Behera, H. S. ^{[1
]}

机构：

[1] Veer Surendra Sai Univ Technol, Dept Comp Sci & Engn, Burla, Odisha, India

来源：

PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON FRONTIERS OF INTELLIGENT COMPUTING: THEORY AND APPLICATIONS (FICTA) 2014, VOL 1 | 2015年 / 327卷

关键词：

Classification; Functional Link Artificial Neural Network (FLANN); Gradient Descent Learning; Particle Swarm Optimization; Genetic Algorithm; ARTIFICIAL NEURAL-NETWORK;

D O I：

10.1007/978-3-319-11933-5_84

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, it is an attempt to design a PSO & GA based FLANN model (PSO-GA-FLANN) for classification with a hybrid Gradient Descent Learning (GDL). The PSO, GA and the gradient descent search are used iteratively to adjust the parameters of FLANN until the error is less than the required value. Accuracy and convergence of PSO-GA-FLANN is investigated and compared with FLANN, GA-based FLANN and PSO-based FLANN. These models have been implemented and results are statistically analyzed using ANOVA test in order to get significant result. To obtain generalized performance, the proposed method has been tested under 5-fold cross validation.

引用

页码：745 / 754

页数：10

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