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

• Published in: Complexity (New York, N.Y.) Vol. 2018; no. 2018; pp. 1 - 21
• Main Authors: Chen, Shuting, Tan, Dapeng
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc; Hindawi Limited; Hindawi-Wiley
• Subjects: Algorithms; Analysis; Artificial neural networks; Back propagation; Clustering; Cognition; Cognition & reasoning; Combinatorial analysis; Computational linguistics; Computer simulation; Damage tolerance; Fault diagnosis; Fault tolerance; Inference; Information processing; Knowledge; Language processing; Learning theory; Machine learning; Mathematical models; Mathematical problems; Memory; Methods; Natural language interfaces; Neural networks; Pattern recognition; Radio communications; Semantics; Simulated annealing
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2018/6264124

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.