Development of Cognitive Capabilities for Smart Home Using a Self-Organizing Fuzzy Neural Network
Robot Control, Volume # 10 | Part# 1
Ray, Anjan Kumar; Leng, Gang; McGinnity, T.M.; Coleman, Sonya; Maguire, Liam
Digital Object Identifier (DOI)
Professional and domestic services; Modeling and identification
A smart home requires cognitive assistance to analyze and understand the behavior in this sensory rich environment. In this paper we explore the potential of a self-organizing fuzzy neural network (SOFNN) as a core component of a cognitive system for a smart home environment. We develop a cognitive reasoning module that has the ability to adapt its neuronal structure through adding and pruning of neurons according to the incoming data. The SOFNN rules explore the relations of the inputs and the desired reasoning outputs. The network is trained with realistic synthesized data to show its adaptation capability and is tested with unseen data to validate its cognitive capabilities. We outline the theoretical development and describe the results achieved. This initial implementation of the cognitive module demonstrates the potential of the architecture and will serve as a very important test-bed for future work.
Bregman D. (2010). Smart home intelligence - the ehome that learns. International journal of smart home , vol. 4, no.4. Chen L., and Nugent C. (2010). Situation aware cognitive assistance in smart homes. Journal of mobile multimedia ,vol. 6, no. 3, 263-280. Cho K. B., and Wang B. H. (1996). Radial basis function based adaptive fuzzy systems and their applications to identification and prediction, Fuzzy Sets and Systems, vol. 83, 325-339. Ferreira, P. M. and Ruano, A. E. (2009). Online sliding window methods for process model adaptation. IEEE transactions on instrumentation and Measurement , vol. 58, no. 9, 3012-3020. Gaddam A., Mukhopadhyay S. C., and Gupta G. S. (2011). Elder care based on cognitive sensor network. IEEE sensors journal , vol. 11, no. 3. Izzeldin, H., Asirvadam, V.S., and Saad, N. (2011). Online sliding-window based for training MLP networks using advanced conjugate gradient. IEEE 7th International colloquium on signal processing and its applications.112-116. Jakkula V., and Cook D. J. (2011). Detecting anomaloussensor events in smart home data for enhancing the living experience, AAAI workshop , 33-37. Jang J. S. R. (1993). Jang, Anfis: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, 665-684. Leng G., McGinnity T. M., and Prasad G. (2005). An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets and Systems, vol. 150, no. 2, 211-243. Leng G., Zeng X-J., and Keane J. A. (2012). An improved approach of self-organising fuzzy neural network based on similarity measures, Evolving Systems, vol. 3, 19?30. Mastrogiovanni F., Sgorbissa A., and Zaccaria R. (2010). A cognitive model for recognizing human behaviours in smart homes. Ann. Telecommunication, vol. 65, 523?538. Roy P. C., Giroux S., Bouchard B., Bouzouane A., Phua C., Tolstikov A., and Biswas J. (2010). Possibilistic behavior recognition in smart homes for cognitive assistance, Twenty-fourth AAAI workshop, 53-60. RUBICON project. (2011). EU FP7 project . FP7 Challenge 2, Cognitive Systems and Robotics. Available:http://www.fp7rubicon.eu Son J. Y., Park J. H., Moon K. D., and Lee Y. H. (2011). Resource-aware smart home management system by constructing resource relation graph. IEEE transactions on consumer electronics , vol. 57, no. 3. Takagi, T., and Sugeno M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics , vol. 15, no. 1, 116-132. Wang N., Er M. J., and Meng X. (2009). A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks, Neurocomputing, vol. 72, no. 16-18, 3818?3829. Wang W. Y., Chuang C. C., Lai Y. S., and Wang Y. H. (2005). A context-aware system for smart home applications. EUC workshops , LNCS 3823, 298 ? 305. Wu S., Er M. J., and Y. Gao. (2001). A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks, IEEE Transactions on Fuzzy Systems , vol. 9, no. 4, 578-594