PSI - Issue 5

Alexander Serov et al. / Procedia Structural Integrity 5 (2017) 1160–1167 Alexander Serov / Structural Integrity Procedia 00 (2017) 000 – 000

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set of disconnected neural sub-networks. These models describe dynamic features of the world. On the next stage of evolution cognitive system must unite these multiple time models and construct true time-and-space model.

5. Conclusions

In current paper we represent results of our activities on development of cognitive system on the basis of model of Cognitive Sensor. Main feature of proposed CS-DANN architecture is the dependence of architecture of network upon the time. We believe that the main advantage of proposed method is a significant degree of generality. There is no need to enter information about the specifics of phenomena in the observable world inside the model; this information can be embedded in the methods used to preprocess the signal. Thus resulting architecture of cognitive system becomes universal. It includes cognitive core which process input data and evolves in a way which is non-dependent upon the architecture and technology of perceptive subsystem. And it includes layer of architecture which was preliminary adapted to specific observable reality. On the next stage of work our research group plans to finish development of numerical method able to simulate all stages of artificial neural network evolution as a single process. Most interesting areas for our further work include development of layered Machine Learning techniques and techniques for autonomous automatic exploration. Abdelwahab, S., Ojha, V., Abraham, A., 2016. Ensemble of Flexible Neural Trees for Predicting Risk in Grid Computing Environment. In: Innovations in Bio-Inspired Computing and Applications. Springer International Publishing, pp. 151-161. Bengio, Y., Goodfellow, I., Courville, A., 2015. Deep learning. Nature, 521, 436-444. Bengio, Y., 2009. Learning deep architectures for AI. Foundations and trends in Machine Learning, 2(1), 1-127. Bornschein, J., Shabanian, S., Fischer, A., Bengio, Y., 2016, May. Bidirectional Helmholtz Machines. In: Proceedings of The 33rd International Conference on Machine Learning, pp. 2511-2519. Chang, F., Markmiller, J., Yang, J., Kim, Y., 2011. Structural health monitoring. System Health Management: With Aerospace Applications, pp. 419-428. Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The" wake-sleep" algorithm for unsupervised neural networks. Science, 268(5214), 1158. Jin, X., Furber, S. B., Woods, J., 2008, June. Efficient modelling of spiking neural networks on a scalable chip multiprocessor. In: Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on. IEEE, pp. 2812-2819 Kim, S., Pakzad, S., Culler, D., Demmel, J., Fenves, G., Glaser, S., Turon, M., 2007, April. Health monitoring of civil infrastructures using wireless sensor networks. In: Proceedings of the 6th international conference on Information processing in sensor networks, ACM, pp. 254-263. Maass, W., 1997. Networks of spiking neurons: the third generation of neural network models. Neural networks, 10(9), 1659-1671. Marzat, J., Piet-Lahanier, H., Damongeot, F., Walter, E., 2012. Model-based fault diagnosis for aerospace systems: a survey, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, SAGE Publications, 226(10), 1329-1360. Merolla, P., Arthur, J., Alvarez-Icaza, R., Cassidy, A., Sawada, J., Akopyan, F., Jackson, B., Imam, N., Guo, C., Nakamura, Y., Brezzo, B., Vo, I., Esser, S., Appuswamy, R., Taba, B., Amir, A., Flickner, M., Risk, W., Manohar, R., Modha, D., 2014. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197), 668-673. Price, D., Scott, D., Edwards, G., Batten, A., Farmer, A., Hedley, M., Johnson, M., Lewis, C., Poulton, G., Prokopenko, M., Valencia, P., Wang, P., 2003. An integrated health monitoring system for an ageless aerospace vehicle. In: Structural health monitoring 2003: from diagnostics & prognostics to structural health management, Chang, F.-K. (Ed). DEStec Publications, Lancaster, Pennsylvania, pp. 310-318. Pyle, R., Rosenbaum, R., 2017. Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks. Physical Review Letters, 118(1), 018103. Salakhutdinov, R., Mnih, A., Hinton, G., 2007, June. Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on Machine learning. ACM, pp. 791-798. Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural networks, 61, 85-117. Serov, A., 2016, July. Application of principles of Artificial General Intelligence in Structural Health Monitoring, 8th European Workshop On Structural Health Monitoring (EWSHM 2016), Bilbao, Spain, paper #22. Smolensky, P., 1986. Information Processing in Dynamical Systems: Foundations of Harmony Theory, CU-CS-321-86. Weiser, M., 1994, March. Ubiquitous computing. In ACM Conference on Computer Science, p. 418. References