A networked smart home system based on recurrent neural networks and reinforcement learning

References

• Al-Ali, A. R., Zualkernan, I. A., Rashid, M., Gupta, R.，Alikarar, M. (2017). A smart home energy management system using IoT and big data analytics approach. IEEE Trans Consum Electr, 63(4), 426–434. https://doi.org/https://doi.org/10.1109/TCE.2017.015014
   Web of Science ®Google Scholar
• Ansarey, M., Shariat Panahi, M., Ziarati, H., Mahjoob, M. (2014). Optimal energy management in a dual-storage fuel-cell hybrid vehicle using multi-dimensional dynamic programming. Journal Power Sources, 250, 359–371. https://doi.org/https://doi.org/10.1016/j.jpows-our.2013.10.145
   Web of Science ®Google Scholar
• Babou, C. S. M., Fall, D., Kashihara, S., INiang, I., Kadobayashi, Y. (2018). Home edge computing (HEC): Design of a new edge computing technology for achieving ultra-low latency, edge computing–EDGE. Springer.
   Google Scholar
• Barsoum, A. F., & Hasan, M. A. (2015). Provable multicopy dynamic data possession in cloud computing systems. IEEE Trans Inf Forensics Security, 10(3), 485–497. https://doi.org/https://doi.org/10.1109/TIFS.2014.2384391
   Web of Science ®Google Scholar
• Chen, Q., Guo, X., & Bai, H. (2017). Semantic-based topic detection using Markov decision processes. Neurocomputing, 242, 40–50. https://doi.org/https://doi.org/10.1016/j.neucom.2017.02.020
   Web of Science ®Google Scholar
• Ding, C. X., Sun, Y., & Zhu, Y. G. (2017). A NN-based hybrid intelligent algorithm for a discrete nonlinear uncertain optimal control problem. Neural Processing Letters, 45(2), 457–473. https://doi.org/https://doi.org/10.1007/s11063-016-9536-8
   Web of Science ®Google Scholar
• Doshi-Velez, F. (2009). The infinite partially observable markov decision process. In Proceedings of 23rd Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada., 22, 477–485.
   Google Scholar
• Guo, S. K., Liu, Y. Q., Chen, R., Sun, X., & Wang, X. X. (2019). Improved smote algorithm to deal with imbalanced activity classes in smart homes. Neural Processing Letters, 50(2), 1503–1526. https://doi.org/https://doi.org/10.1007/s11063-018-9940-3
   Web of Science ®Google Scholar
• Hao, J., Bouzouane, A., & Gaboury, S. (2018). Recognizing multi-resident activities in non-intrusive sensor-based smart homes by formal concept analysis. Neurocomputing, 318, 75–89. https://doi.org/https://doi.org/10.1016/j.neucom.2018.08.033
   Web of Science ®Google Scholar
• Haykin, S. (1999). Neural networks-A comprehensive foundation, 2nd edition. Prentice-Hall.
   Google Scholar
• Huaguang, Z., & Zhanshan, W. (2009). New delay-dependent criterion for the stability of recurrent neural networks with time-varying delay. Science China Information Sciences, 52(6), 942–948. https://doi.org/https://doi.org/10.1007/s11432-009-0100-2
   Web of Science ®Google Scholar
• Keshtkar, A., & Arzanpour, S. (2017). An adaptive fuzzy logic system for residential energy management in smart grid environments. Applied Energy, 186, 68–81. https://doi.org/https://doi.org/10.1016/j.apenergy.2016.11.028
   Web of Science ®Google Scholar
• Li, Z. (2019). An adaptive overload threshold selection process using Markov decision processes of virtual machine in cloud data center. Cluster Computing, 22(S2), 3821–3833. https://doi.org/https://doi.org/10.1007/s10586-018-2408-4
   Google Scholar
• Mathe, S., Pirinen, A., & Sminchisescu, C. (2016). Reinforcement learning for visual object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2894–2902.
   Google Scholar
• Nguyen, N. D., Nguyen, T., & Nahavandi, S. (2019). Multi-agent behavioral control system using deep reinforcement learning. Neurocomputing, 359, 58–68. https://doi.org/https://doi.org/10.1016/j.neucom.2019.05.062
   Web of Science ®Google Scholar
• Qiao, Y., Si, Z., Zhang, Y., Abdesslem, F.  B., Zhang, X., Yang, J. (2018). A hybrid markov-based model for human mobility prediction. Neurocomputing, 278, 99–109. https://doi.org/https://doi.org/10.1016/j.neucom.2017.05.101
   Web of Science ®Google Scholar
• Redouane, B., & Cherif, B. M. (2018). Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov process. Innovations System Software Eng, 14(3), 229–244. https://doi.org/https://doi.org/10.1007/s11334-018-0316-7
   Web of Science ®Google Scholar
• Sun, X., Wu, P., & Hoi, S. C. H. (2018). Face detection using deep learning: An improved faster RCNN approach. Neurocomputing, 299, 42–50. https://doi.org/https://doi.org/10.1016/j.neucom.2018.03.030
   Web of Science ®Google Scholar
• Teich, T., Roessler, F., Kretz, D., Franke, S. (2014). Design of a prototype neural network for smart homes and energy efficiency. Procedia Engineering, 69, 603–608. https://doi.org/https://doi.org/10.1016/j.proeng.2014.03.032
   Google Scholar
• Tu, Z. W., Cao, J. D., Alsaedi, A., Alsaadi, F. E., & Hayat, T. (2016). Global Lagrange stability of complex-valued neural networks of neutral type with time-varying delays. Complexity, 21(S2), 438–450. https://doi.org/https://doi.org/10.1002/cplx.21823
   Web of Science ®Google Scholar
• Wu, Y. F., & Yue, D. (2019). Robust adaptive neural network control for a class of multiple-input multiple-output nonlinear time delay system with hysteresis inputs and dynamic uncertainties. Asian Journal Control, 21(5), 2330–2339. https://doi.org/https://doi.org/10.1002/asjc.1831
   Web of Science ®Google Scholar
• Xie, W., Xie, D., Zou, Y., Zhang, D., Sun, Z., & Zhang, L. (2020). Deep reinforcement learning for smart home energy management. IEEE Internet of Things Journal, 7(4), 2751–2762. https://doi.org/https://doi.org/10.1109/JIOT.2019.2957289
   Web of Science ®Google Scholar
• Xing, M. L., & Deng, F. Q. (2019). Tracking control for stochastic multi-agent systems based on hybrid event-triggered mechanism. Asian Journal Control, 21(5), 2352–2363. https://doi.org/https://doi.org/10.1002/asjc.1823
   Web of Science ®Google Scholar
• Xu, Q. Y., Zhang, Y. J., & Xiao, S. Y. (2019). Event-triggered guaranteed cost consensus of networked singular multi-agent systems. Asian Journal Control, 21(5), 2425–2440. https://doi.org/https://doi.org/10.1002/asjc.1848
   Web of Science ®Google Scholar
• Yan, M. M., Qiu, J. L., Chen, X. Y., Chen, X., Yang, C. D., Zhang, A. C., & Alsaadi, F. (2018). The global exponential stability of the delayed complex-valued neural networks with almost periodic coefficients and discontinuous activations. Neural Processing Letters, 48(1), 577–601. https://doi.org/https://doi.org/10.1007/s11063-017-9736-x
   Web of Science ®Google Scholar
• Yin, Z., He, W., Yang, C., Sun, C. (2018). Control design of a marine vessel system using reinforcement learning. Neurocomputing, 311, 353–362. https://doi.org/https://doi.org/10.1016/j.neucom.2018.05.061
   Web of Science ®Google Scholar
• Yu, L., Jiang, T., & Zou, Y. (2019). Online energy management for a sustainable smart home with an HVAC load and random occupancy. IEEE Transactions on Smart Grid, 10(2), 1646–1659. https://doi.org/https://doi.org/10.1109/TSG.2017.2775209
   Web of Science ®Google Scholar
• Yu, L., Qin, S., Zhang, M., Shen, C., Jiang, T., & Guan, X. (2021). A review of deep reinforcement learning for smart building energy management. IEEE Internet of Things Journal, 8(15), 12046–12063. https://doi.org/https://doi.org/10.1109/JIOT.2021.3078462
   Web of Science ®Google Scholar
• Zhang, D., Kou, K. I., Liu, Y., Cao, J. (2017). Decomposition approach to the stability of recurrent neural networks with asynchronous time delays in quaternion field. Neural Networks, 94, 55–66. https://doi.org/https://doi.org/10.1016/j.neunet.2017.06.014
   PubMed Web of Science ®Google Scholar
• Zheng, L., & Cho, S. Y. (2011). A modified memory-based reinforcement learning method for solving POMDP problems. Neural Processing Letters, 33(2), 187–200. https://doi.org/https://doi.org/10.1007/s11063-011-9172-2
   Web of Science ®Google Scholar
• Zheng, L., Cho, S. Y., & Quek, C. (2008). A memory-based reinforcement learning algorithm for partially observable Markovian decision processes. In: Proceedings of IEEE world congress on computational intelligence. Hong Kong, pp. 800–805.
   Google Scholar