Table of Contents Author Guidelines Submit a Manuscript
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.

Linked References

  1. A. Newell and H. A. Simon, “Computer simulation of human thinking,” Science, vol. 134, no. 3495, pp. 2011–2017, 1961. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Kvassay, P. Krammer, L. Hluchý, and B. Schneider, “Causal analysis of an agent-based model of human behaviour,” Complexity, vol. 2017, Article ID 8381954, 18 pages, 2017. View at Google Scholar · View at MathSciNet
  3. A. Newell, “The knowledge level,” Artificial Intelligence, vol. 18, no. 1, pp. 87–127, 1982. View at Publisher · View at Google Scholar · View at Scopus
  4. D.-P. Tan, S.-M. Ji, and M.-S. Jin, “Intelligent computer-aided instruction modeling and a method to optimize study strategies for parallel robot instruction,” IEEE Transactions on Education, vol. 56, no. 3, pp. 268–273, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Tan, L. Zhang, and Q. Ai, “An embedded self-adapting network service framework for networked manufacturing system,” Journal of Intelligent Manufacturing, pp. 1–18, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Rómoli, M. Serrano, F. Rossomando, J. Vega, O. Ortiz, and G. Scaglia, “Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor,” Complexity, vol. 2017, Article ID 9391879, 16 pages, 2017. View at Google Scholar · View at MathSciNet
  7. J. Chen, J. Yang, J. Zhao, F. Xu, Z. Shen, and L. Zhang, “Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method,” Neurocomputing, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. D. P. Tan, S. T. Chen, G. J. Bao, and L. B. Zhang, “An embedded lightweight GUI component library and the ergonomics optimization method for industry process monitoring,” in Frontiers of Information Technology and Electronic Engineering, 2017. View at Google Scholar
  9. M. Diykh, Y. Li, and P. Wen, “Classify epileptic EEG signals using weighted complex networks based community structure detection,” Expert Systems with Applications, vol. 90, pp. 87–100, 2017. View at Publisher · View at Google Scholar
  10. P. Sturt, F. Costa, V. Lombardo, and P. Frasconi, “Learning first-pass structural attachment preferences with dynamic grammars and recursive neural networks,” Cognition, vol. 88, no. 2, pp. 133–169, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. J. R. Vokey and P. A. Higham, “Opposition logic and neural network models in artificial grammar learning,” Consciousness and Cognition, vol. 13, no. 3, pp. 565–578, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. R. J. Tunney and D. R. Shanks, “Does opposition logic provide evidence for conscious and unconscious processes in artificial grammar learning?” Consciousness and Cognition, vol. 12, no. 2, pp. 201–218, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. P. A. Higham and J. R. Vokey, “The controlled application of a strategy can still produce automatic effects: reply to redington,” Journal of Experimental Psychology: General, vol. 129, no. 4, pp. 476–480, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Hallner and M. Hasenbring, “Classification of psychosocial risk factors (yellow flags) for the development of chronic low back and leg pain using artificial neural network,” Neuroscience Letters, vol. 361, no. 1-3, pp. 151–154, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. G. B. Kaplan, N. S. Şengör, H. Gürvit, I. Genç, and C. Güzeliş, “A composite neural network model for perseveration and distractibility in the Wisconsin card sorting test,” Neural Networks, vol. 19, no. 4, pp. 375–387, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Hélie, S. Chartier, and R. Proulx, “Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning,” Cognitive Systems Research, vol. 7, no. 4, pp. 357–371, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. D. S. Levine, “Neural network modeling of emotion,” Physics of Life Reviews, vol. 4, no. 1, pp. 37–63, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Chartier, P. Renaud, and M. Boukadoum, “A nonlinear dynamic artificial neural network model of memory,” New Ideas in Psychology, vol. 26, no. 2, pp. 252–277, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Tsagkaris, A. Katidiotis, and P. Demestichas, “Neural network-based learning schemes for cognitive radio systems,” Computer Communications, vol. 31, no. 14, pp. 3394–3404, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Schmidt-Atzert, S. Krumm, and D. Lubbe, “Toward Stable Predictions of Apprentices' Training Success: Can Artificial Neural Networks Outperform Linear Predictions?” Journal of Personnel Psychology, vol. 10, no. 1, pp. 34–42, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Alexandridis and Y. Maru, “Collapse and reorganization patterns of social knowledge representation in evolving semantic networks,” Information Sciences, vol. 200, pp. 1–21, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. E. Grossi, A. Compare, and M. Buscema, “The concept of individual semantic maps in clinical psychology: A feasibility study on a new paradigm,” Quality & Quantity, vol. 48, no. 1, pp. 15–35, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. E. O. Neftci, C. Augustine, S. Paul, and G. Detorakis, “Event-driven random back-propagation: enabling neuromorphic deep learning machines,” Frontiers in Neuroscience, vol. 11, Article ID 324, 2017. View at Publisher · View at Google Scholar
  24. O. K. Oyedotun and A. Khashman, “Prototype-incorporated emotional neural network,” IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1–13. View at Publisher · View at Google Scholar
  25. D.-P. Tan, P.-Y. Li, Y.-X. Ji, D.-H. Wen, and C. Li, “SA-ANN-based slag carry-over detection method and the embedded WME platform,” IEEE Transactions on Industrial Electronics, vol. 60, no. 10, pp. 4702–4713, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. V. Lukovac, D. Pamučar, M. Popović, and B. Đorović, “Portfolio model for analyzing human resources: An approach based on neuro-fuzzy modeling and the simulated annealing algorithm,” Expert Systems with Applications, vol. 90, pp. 318–331, 2017. View at Publisher · View at Google Scholar
  27. T. Yang, A. A. Asanjan, M. Faridzad, N. Hayatbini, X. Gao, and S. Sorooshian, “An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis,” Information Sciences, vol. 418-419, pp. 302–316, 2017. View at Publisher · View at Google Scholar
  28. A. Demiroren, H. L. Zeynelgil, and N. S. Sengor, “Automatic generation control using ANN technique for multi-area power system with SMES units,” Electric Power Components and Systems, vol. 32, no. 2, pp. 193–213, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. C. Li, S.-M. Ji, and D.-P. Tan, “Multiple-loop digital control method for a 400-hz inverter system based on phase feedback,” IEEE Transactions on Power Electronics, vol. 28, no. 1, pp. 408–417, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. D.-P. Tan, S.-M. Ji, and Y.-Z. Fu, “An improved soft abrasive flow finishing method based on fluid collision theory,” The International Journal of Advanced Manufacturing Technology, vol. 85, no. 5-8, pp. 1261–1274, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. A. C. Zimmermann, C. L. N. Veiga, and L. S. Enemas, “Unambiguous signal processing and measuring range extension for fiber Bragg gratings sensors using artificial neural networks-a temperature case,” IEEE Sensors Journal, vol. 8, no. 7, pp. 1229–1235, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. S. Hussain and A. AlAlili, “A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks,” Applied Energy, vol. 208, pp. 540–550, 2017. View at Publisher · View at Google Scholar
  33. D. P. Tan, T. Yang, J. Zhao, and S. M. Ji, “Free sink vortex Ekman suction-extraction evolution mechanism,” Acta Physica Sinica, vol. 65, no. 5, Article ID 054701, 2016. View at Google Scholar
  34. J. García, C. Pope, and F. Altimiras, “A distributed K-means segmentation algorithm applied to lobesia botrana recognition,” Complexity, vol. 2017, Article ID 5137317, 14 pages, 2017. View at Publisher · View at Google Scholar
  35. D. Tan, S. Ji, P. Li, and X. Pan, “Development of vibration style ladle slag detection methods and the key technologies,” Science China Technological Sciences, vol. 53, no. 9, pp. 2378–2387, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. N. Zheng, L. Su, D. Zhang, L. Gao, M. Yao, and Z. Wu, “A computational model for ratbot locomotion based on cyborg intelligence,” Neurocomputing, vol. 170, pp. 92–97, 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Bahrami and F. Doulati Ardejani, “Prediction of pyrite oxidation in a coal washing waste pile using a hybrid method, coupling artificial neural networks and simulated annealing (ANN/SA),” Journal of Cleaner Production, vol. 137, pp. 1129–1137, 2016. View at Publisher · View at Google Scholar · View at Scopus
  38. M. L. Dantas Dias and A. R. Rocha Neto, “Training soft margin support vector machines by simulated annealing: A dual approach,” Expert Systems with Applications, vol. 87, pp. 157–169, 2017. View at Publisher · View at Google Scholar
  39. Z. Wu, N. Zheng, S. Zhang, X. Zheng, L. Gao, and L. Su, “Maze learning by a hybrid brain-computer system,” Scientific Reports, vol. 6, no. 1, Article ID 31746, 2016. View at Publisher · View at Google Scholar
  40. N. Sengupta and N. Kasabov, “Spike-time encoding as a data compression technique for pattern recognition of temporal data,” Information Sciences, vol. 406-407, pp. 133–145, 2017. View at Publisher · View at Google Scholar
  41. D. Tan, Y. Ni, and L. Zhang, “Two-phase sink vortex suction mechanism and penetration dynamic characteristics in ladle teeming process,” Journal of Iron and Steel Research, International, vol. 24, no. 7, pp. 669–677, 2017. View at Publisher · View at Google Scholar
  42. L. Chen, W. Chew, and M. Garland, “Spectral pattern recognition of in situ FT-IR spectroscopic reaction data using minimization of entropy and spectral similarity (MESS): Application to the homogeneous rhodium catalyzed hydroformylation of isoprene,” Applied Spectroscopy, vol. 57, no. 5, pp. 491–498, 2003. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Jurek, “Recent developments of the syntactic pattern recognition model based on quasi-context sensitive languages,” Pattern Recognition Letters, vol. 26, no. 7, pp. 1011–1018, 2005. View at Publisher · View at Google Scholar · View at Scopus
  44. B. X. Du, Y. Wu, Y. H. Lu, and X. Zhang, “PD pattern recognition of acoustic signals from polarity characteristics in generators,” in Proceedings of the ICPADM 2006 - 8th International Conference on Properties and Applications of Dielectric Materials, pp. 710–713, Indonesia, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  45. D. P. Tan and L. B. Zhang, “A WP-based nonlinear vibration sensing method for invisible liquid steel slag detection,” Sensors and Actuators B: Chemical, vol. 202, pp. 1257–1269, 2014. View at Publisher · View at Google Scholar
  46. I. Trendafilova, “Pattern recognition methods for damage diagnosis in structures from vibration measurements,” Key Engineering Materials, no. 204-205, pp. 85–94, 2001. View at Publisher · View at Google Scholar · View at Scopus
  47. I. Trendafilova and W. Heylen, “Categorisation and pattern recognition methods for damage localisation from vibration measurements,” Mechanical Systems and Signal Processing, vol. 17, no. 4, pp. 825–836, 2003. View at Publisher · View at Google Scholar · View at Scopus
  48. N. Zheng, M. Jin, H. Hong, L. Huang, Z. Gu, and H. Li, “Real-time and precise insect flight control system based on virtual reality,” IEEE Electronics Letters, vol. 53, no. 6, pp. 387–389, 2017. View at Publisher · View at Google Scholar · View at Scopus
  49. M. R. Widyanto, H. Nobuhara, K. Kawamoto, K. Hirota, and B. Kusumoputro, “Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA),” Applied Soft Computing, vol. 6, no. 1, pp. 72–84, 2005. View at Publisher · View at Google Scholar · View at Scopus
  50. R. G. Brereton, “Steepest ascent, steepest descent, and gradient methods,” Comprehensive Chemometrics, vol. 1, pp. 577–590, 2010. View at Publisher · View at Google Scholar · View at Scopus
  51. W. Wang, P. H. A. J. M. V. Gelder, J. K. Vrijling, and J. Ma, “Forecasting daily streamflow using hybrid ANN models,” Journal of Hydrology, vol. 324, no. 1–4, pp. 383–399, 2006. View at Publisher · View at Google Scholar · View at Scopus
  52. E. Soria-Olivas, J. D. Martin-Guerrero, A. J. Serrano-López, J. Calpe-Maravilla, J. Vila-Francés, and G. Camps-Valls, “Efficient pruning of multilayer perceptrons using a fuzzy sigmoid activation function,” Neurocomputing, vol. 69, no. 7-9, pp. 909–912, 2006. View at Publisher · View at Google Scholar · View at Scopus
  53. Z. J. Rong, B. B. Dan, and J. G. Yi, “A BP neural network predictor model for desulfurizing molten iron,” Lecture Notes in Computer Science, vol. 3584, pp. 728–735, 2005. View at Google Scholar
  54. P. Y. Jin, B. Y. Gao, H. J. Lu, and L. N. Chen, “A method of extreme learning machine based on restricted Boltzmann machine,” Mathematics in Practice and Theory, vol. 46, no. 11, pp. 157–161, 2016. View at Google Scholar
  55. X.-H. He, D. Wang, Y.-F. Li, and C.-H. Zhou, “A novel bearing fault diagnosis method based on gaussian restricted boltzmann machine,” Mathematical Problems in Engineering, vol. 2016, Article ID 2957083, 8 pages, 2016. View at Publisher · View at Google Scholar
  56. J. Kim, J. Kim, K. Shin, H. Lee, and S. Park, “ANN-based tensile force estimation for pre-stressed tendons of PSC girders using FBG/EM hybrid sensing,” Insight - Non-Destructive Testing and Condition Monitoring, vol. 59, no. 10, pp. 544–552, 2017. View at Publisher · View at Google Scholar
  57. R. Song and S. Chen, “A self-tuning proportional-integral-derivative-based temperature control method for draw-texturing-yarn machine,” Mathematical Problems in Engineering, vol. 2017, Article ID 1864321, 17 pages, 2017. View at Google Scholar · View at MathSciNet
  58. S. Kobashi, N. Kamiura, Y. Hata, and F. Miyawaki, “Volume-quantization-based neural network approach to 3D MR angiography image segmentation,” Image and Vision Computing, vol. 19, no. 4, pp. 185–193, 2001. View at Publisher · View at Google Scholar · View at Scopus
  59. R. Yahiaoui, F. Alilat, and S. Loumi, “Parallelization of Fuzzy ARTMAP architecture on FPGA: multispectral classification of ALSAT-2A images,” IEEE Transactions on Industrial Electronics, vol. 64, no. 12, pp. 9487–9495, 2017. View at Publisher · View at Google Scholar
  60. S. Zhang, T. Wang, J. Dong, and H. Yu, “Underwater image enhancement via extended multi-scale Retinex,” Neurocomputing, vol. 245, pp. 1–9, 2017. View at Publisher · View at Google Scholar · View at Scopus
  61. D. Tan, L. Li, Y. Zhu, S. Zheng, and X. Jiang, “An embedded cloud database service method for distributed industry monitoring,” IEEE Transactions on Industrial Informatics, vol. PP, no. 99, pp. 1–1, 2017. View at Publisher · View at Google Scholar