Integrated Feature Selection and Parameter Optimization for Evolving Spiking Neural Networks using Quantum Inspired Particle Swarm Optimization

被引：11

作者：

Hamed, Haza Nuzly Abdull ^{[1
]}

Kasabov, Nikola ^{[1
]}

Shamsuddin, Siti Mariyam ^{[2
]}

机构：

[1] Auckland Univ Technol, KEDRI, Auckland, New Zealand

[2] Univ Teknol Malaysia, Soft Computing Res Grp, Johor Baharu, Johor, Malaysia

来源：

2009 INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION | 2009年

关键词：

Evolving Spiking Neural Network; Particle Swarm; Quantum Computing; Feature Optimization; Parameter Optimization;

D O I：

10.1109/SoCPaR.2009.139

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a novel method for optimizing features and parameters in the Evolving Spiking Neural Network (ESNN) using Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals the interesting concept of QiPSO in which information is represented as binary structures. The mechanism simultaneously optimizes the ESNN parameters and relevant features using wrapper approach. A synthetic dataset is used to evaluate the performance of the proposed method. The results show that QiPSO yields promising outcomes in obtaining the best combination of ESNN parameters as well as in identifying the most relevant features.

引用

页码：695 / +

页数：2

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