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Complexity
Volume 2018, Article ID 6264124, 21 pages
https://doi.org/10.1155/2018/6264124
Research Article

A SA-ANN-Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

1Hangzhou Medical College, Hangzhou 310053, China
2Zhejiang University of Technology, Hangzhou 310032, China

Correspondence should be addressed to Dapeng Tan; moc.qq@85231987

Received 12 July 2017; Revised 2 November 2017; Accepted 26 November 2017; Published 4 January 2018

Academic Editor: Michele Scarpiniti

Copyright © 2018 Shuting Chen and Dapeng Tan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial neural networks (ANNs) are the important approaches for researching human cognition process. However, current ANNs-based cognition methods cannot address the problems of complex information understanding and fault-tolerant learning. Here we present a modeling method for cognition mechanism based on a simulated annealing–artificial neural network (SA-ANN). Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. Finally, three groups of numerical instances for knowledge clustering are provided, and three comparative experiments are performed by self-developed cognition software. The simulated results show that the proposed method can increase the convergence rate by more than 20%, compared with the back-propagation (BP), SA, and restricted Boltzmann machines based extreme learning machine (RBM-ELM) algorithms. The comparative cognition experiments prove that the method can obtain better performances of information understanding and fault-tolerant learning, and the cognition accuracies for original sample, damaged sample, and transformed sample can reach 99.6%, 99.2%, and 97.1%, respectively.