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

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  • Shuting Chen
  • Dapeng Tan

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.

Suggested Citation

  • Shuting Chen & Dapeng Tan, 2018. "A SA-ANN-Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm," Complexity, Hindawi, vol. 2018, pages 1-21, January.
  • Handle: RePEc:hin:complx:6264124
    DOI: 10.1155/2018/6264124
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    1. Enzo Grossi & Angelo Compare & Massimo Buscema, 2014. "The concept of individual semantic maps in clinical psychology: a feasibility study on a new paradigm," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 15-35, January.
    2. Santiago Rómoli & Mario Serrano & Francisco Rossomando & Jorge Vega & Oscar Ortiz & Gustavo Scaglia, 2017. "Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor," Complexity, Hindawi, vol. 2017, pages 1-16, September.
    3. Marcel Kvassay & Peter Krammer & Ladislav Hluchý & Bernhard Schneider, 2017. "Causal Analysis of an Agent-Based Model of Human Behaviour," Complexity, Hindawi, vol. 2017, pages 1-18, January.
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    Cited by:

    1. Li-Nan Zhu & Peng-Hang Li & Xiao-Long Zhou, 2019. "IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing," Complexity, Hindawi, vol. 2019, pages 1-21, February.

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