SA-ANN Based Cognition Mechanism Modeling and the Improved Recognition Algorithm

CHEN Shu-ting; TAN Da-peng

doi:10.3969/j.issn.0372-2112.2018.08.029

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SA-ANN Based Cognition Mechanism Modeling and the Improved Recognition Algorithm

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- SA-ANN Based Cognition Mechanism Modeling and the Improved Recognition Algorithm
- Acta Electronica Sinica Vol. 46, Issue 8, Pages: 2011-2019(2018)
- 作者机构：
  
  1. 杭州医学院基础医学部,浙江,杭州,310053
  2. 浙江工业大学机械工程学院,浙江,杭州,310032
  3. 杭州医学院基础医学部,浙江,杭州,310053
  4. 浙江工业大学机械工程学院,浙江,杭州,310032
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51775501);Outstanding Youth Science Fund of Zhejiang Province (No.LR16050001);Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (No.2015KYA067);Education Science Planning Project of Zhejiang Province (No.2017SCG386)
- DOI：10.3969/j.issn.0372-2112.2018.08.029
  CLC： TP391B842
- Published Online：25 August 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
CHEN Shu-ting, TAN Da-peng. SA-ANN Based Cognition Mechanism Modeling and the Improved Recognition Algorithm[J]. Acta Electronica Sinica, 2018, 46(8): 2011-2019.
DOI：

CHEN Shu-ting, TAN Da-peng. SA-ANN Based Cognition Mechanism Modeling and the Improved Recognition Algorithm[J]. Acta Electronica Sinica, 2018, 46(8): 2011-2019. DOI： 10.3969/j.issn.0372-2112.2018.08.029.

摘要

人脑认知过程机制建模是人工智能研究领域的重要方向，当前基于统计模板分析与反向传播神经网络（BP-ANN）的认知方法在聚类计算与知识理解方面存在不足.针对上述问题，提出了一种基于模拟退火神经网络（SA-ANN）的认知过程机制建模方法.对人脑认知物理过程及其基本特征进行了分析，建立了面向认知过程的SA-ANN推理模型.提出了一种改进的模拟退火神经网络（ISA-ANN）识别优化算法，对认知过程信息特征提取、知识学习训练等关键环节进行了模拟研究.设计了认知过程机制算例，开发了相应的原型软件系统，对理论结果进行了验证.结果证明，该方法具有较好的聚类性能，可以针对具体测试对象进行准确识别，能够得到相对精确的认知演化规律.

Abstract

Human cognition mechanism modeling was an important research direction of artificial intelligence area.Current modeling methods based statistical modes or back propagation artificial neural network (BP-ANN) have the problems of clustering computation and knowledge understanding.Concerning the issue

a mechanism modeling method for cognition process was proposed based on simulated annealing artificial neural network (SA-ANN).The cognition physical process and its fundamental characteristics were analyzed

and a SA-ANN inference model oriented to cognition process was set up.An improved simulated annealing artificial neural network (ISA-ANN) processing algorithm was put forward

and the critical factors of information character extraction and knowledge clustering for cognition process were simulated.Numerical instances for human cognition were provided

and a prototype prototype software system was developed to verify the theoretical results.Experimental results prove that the proposed method is with better clustering performance

can correctly recognize the testing object selected

and can reveal the evolution regulars of psychological cognition process.

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