Genetic neural network based on adaptive potential well crossover operator and its application in recognition of blue-green algal

Author

Pu, Ziying ; Yao, Zhihong ; Fei, Minrui ; Yin, Xiurong ; Kong, Hainan

Author_Institution

Sch. of Electr. Inf. & Electron. Eng., Shanghai Jiaotong Univ.

Volume

1

fYear

2006

fDate

21-23 June 2006

Firstpage

3203

Lastpage

3207

Abstract

In accordance with issues of high randomness, slow convergence speed and prematurity of genetic algorithm, an adaptive potential well crossover operator is proposed. The operator concludes the wave function of particle in square well with infinite depth in quantum mechanics and the concept of quantization of energy. In the algorithm, the operator and the strategy of deterministic crowding mechanism are used in neural network training. It has been demonstrated by simulation results and the pattern recognition experiment on blue-green algal that the approach not only has the properties of high convergence speed and strong searching ability but also has high efficiency in pattern recognition

Keywords

botany; genetic algorithms; neural nets; pattern recognition; quantum computing; search problems; adaptive potential well crossover operator; blue-green algal recognition; deterministic crowding mechanism; energy quantization; genetic algorithm; genetic neural network; neural network training; pattern recognition; quantum mechanics; searching ability; wave function; Adaptive systems; Genetic algorithms; Genetic engineering; Intelligent networks; Neural networks; Pattern recognition; Potential well; Wave functions; adaptive potential well crossover operator; deterministic crowding mechanism; genetic algorithm; neural network; pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712958

Filename

1712958