A new training strategy for neural network using shuffled frog-leaping algorithm and application to channel equalization

被引：28

作者：

Panda, Sunita ^{[1
]}

Sarangi, Archana ^{[2
]}

Panigrahi, Siba Prasada ^{[3
]}

机构：

[1] Kalam Inst Technol, Berhampur, Odisha, India

[2] SOA Univ, ITER, Bhubaneswar, Odisha, India

[3] CV Raman Coll Engn, Bhubaneswar, Odisha, India

来源：

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS | 2014年 / 68卷 / 11期

关键词：

Neural network; Shuffled frog-leaping algorithm; Channel equalization; ANN; OPTIMIZATION;

D O I：

10.1016/j.aeue.2014.05.005

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];

摘要：

This paper makes use of shuffled frog-leaping algorithm (SFLA) as a training algorithm to train multilayer artificial neural network (ANN). Next, The SFLA ANNs are used for channel equalization. We, in this paper, also introduce SFLA for channel equalization that is formulated as an optimization problem. In short, this paper introduces a novel strategy for training of ANN and also proposes two novel approaches for channel equalization problem using shuffled frog-leaping algorithm (SFLA). The proposed strategies are tested both in time-invariant and time varying channels and interestingly yield better performance than contemporary approaches as evidenced by simulation results. (C) 2014 Elsevier GmbH. All rights reserved.

引用

页码：1031 / 1036

页数：6

共 23 条

[1]

A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization [J].