Earticle

초록

영어

Synergetic neural network (SNN) is a top-down network to explain the phase transition and self-organization in non-equilibrium system. The network parameters have a crucial impact on the recognition performance of synergetic neural network. At present, there is no good way to control and adjust the network parameters. To solve these problems, an improved parameters optimization algorithm based on differential evolution algorithm is proposed and implemented in this paper. There are two main works in this paper. Firstly, a semantic analysis model based on synergetic neural network is presented. Secondly, differential evolution algorithm is used to search the global optimum of network parameters in the corresponding parameter space. The experiments showed that the optimization algorithm can improve the synergetic recognition performance.

목차

Abstract
 1. Introduction
 2. A Brief Introduction to DE and SNN
  2.1 Background of SNN
  2.2 Background of DE
 3. Semantic role labeling based on SNN
  3.1 Feature Selection
  3.2 Semantic Role Labeling Model
 4. A parameters Optimization Algorithm based on Differential Evolution Algorithm
 5. Experiment
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Jianxin Huang [ School of Mathematics Sciences,Huaqiao University, quanzhou, 362021, China ]
• Zhehuang Huang [ School of Mathematics Sciences,Huaqiao University, quanzhou, 362021, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605