The Effect of Bat Population in Bat-BP Algorithm

被引：4

作者：

Nawi, Nazri Mohd. ^{[1
]}

Rehman, Muhammad Zubair ^{[1
]}

Khan, Abdullah ^{[1
]}

机构：

[1] Univ Tun Hussein Onn Malaysia UTHM, Software & Multimedia Ctr, Fac Comp Sci & Informat Technol, Batu Pahat 86400, Johor, Malaysia

来源：

8TH INTERNATIONAL CONFERENCE ON ROBOTIC, VISION, SIGNAL PROCESSING & POWER APPLICATIONS: INNOVATION EXCELLENCE TOWARDS HUMANISTIC TECHNOLOGY | 2014年 / 291卷

关键词：

Bat population; Metaheuristics; Slow convergence; Global minima; Bat algorithm; Back-propagation neural network algorithm; Bat-BP algorithm; Momentum; NEURAL-NETWORKS;

D O I：

10.1007/978-981-4585-42-2_34

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A new metaheuristic based back-propagation algorithm known as Bat-BP is presented in this paper. The proposed Bat-BP algorithm successfully solves the problems like slow convergence to global minima and network stagnancy in back-propagation neural network (BPNN) algorithm. In this paper, the bat population is increased from 10 to 500 bats to detect the performance decline or incline in the Bat-BP algorithm by performing simulations on XOR and OR datasets. The simulation results show that the convergence rate to global minimum in Bat-BP is directly proportional with an increase in bats on 2-bit XOR dataset. In case of 3-bit XOR and 4-bit OR datasets, the results deteriorated with an increase in the bat population.

引用

页码：295 / 302

页数：8

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