Analysis of a hybrid neural network as underlying mechanism for a situation prediction engine

This paper presents the results regarding a technique that can be used as an underlying mechanism for situation prediction. We analysed a hybrid neural network called Multi-output Adaptive Neural Fuzzy Inference System (MANFIS) and compared its predictive ability with a Multi-Layer Perceptron (MLP). The results demonstrate that, depending on the application, the use of neural networks can be considered to be a good approach for situation prediction, when combined with other techniques. Key words: situation, context, prediction, neural networks, MANFIS.

[1]  Juan Carlos Augusto,et al.  Ambient Intelligence—the Next Step for Artificial Intelligence , 2008, IEEE Intelligent Systems.

[2]  Stathes Hadjiefthymiades,et al.  Path Prediction through Data Mining , 2007, IEEE International Conference on Pervasive Services.

[3]  Ahmed Mustafa Elmahalawy,et al.  Anticipation the consumed electrical power in Smart Home using evolutionary algorithms , 2010, 2010 International Conference on Multimedia Computing and Information Technology (MCIT).

[4]  Ajith Abraham,et al.  Adaptation of Fuzzy Inference System Using Neural Learning , 2005 .

[5]  Ernesto Jiménez-Ruiz,et al.  Spatial and Temporal Reasoning for Ambient Intelligence Systems , 2009 .

[6]  Matthias Rauterberg,et al.  Responsive environments: User experiences for ambient intelligence , 2010, J. Ambient Intell. Smart Environ..

[7]  Lucia Vacariu,et al.  Agent based smart house platform with affective control , 2009, EATIS.

[8]  Mohammad Teshnehlab,et al.  Estimating Development Time and Effort of Software Projects by using a Neuro_Fuzzy Approach , 2009 .

[9]  Gwenn Englebienne,et al.  An activity monitoring system for elderly care using generative and discriminative models , 2010, Personal and Ubiquitous Computing.

[10]  Cristiano Andre da Costa Continuum : a context-aware service-based software infrastucture for ubiquitous computing , 2008 .

[11]  Stephen S. Yau,et al.  Development and runtime support for situation-aware application software in ubiquitous computing environments , 2004, Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004..

[12]  James L. Crowley,et al.  Reinforcement Learning of Context Models for a Ubiquitous Personal Assistant , 2009, Ubicomp 2009.

[13]  Jorge Sá Silva,et al.  Percontrol: A pervasive system for educational environments , 2012, 2012 International Conference on Computing, Networking and Communications (ICNC).

[14]  Erik S. Connors,et al.  Situation awareness: State of the art , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[15]  Hans W. Guesgen,et al.  Spatial and Temporal Reasoning for Ambient Intelligence Systems COSIT 2009 Workshop Proceedings , 2009 .

[16]  Simon A. Dobson,et al.  Situation identification techniques in pervasive computing: A review , 2012, Pervasive Mob. Comput..

[17]  Marcel Cremene,et al.  Towards an Affective Aware Home , 2009, ICOST.

[18]  Stephen S. Yau,et al.  Development of situation-aware application software for ubiquitous computing environments , 2002, Proceedings 26th Annual International Computer Software and Applications.

[19]  Anne Hays,et al.  Notes from the editors. , 1997, Journal of motor behavior.

[20]  Albrecht Schmidt,et al.  Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts , 2002, Mob. Networks Appl..

[21]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[22]  Werner Kurschl,et al.  A survey on situation-aware ambient intelligence systems , 2011, J. Ambient Intell. Humaniz. Comput..

[23]  Stathes Hadjiefthymiades,et al.  Predicting the location of mobile users: a machine learning approach , 2009, ICPS '09.

[24]  Stathes Hadjiefthymiades,et al.  An Online Adaptive Model for Location Prediction , 2009, Autonomics.

[25]  Adenauer C. Yamin Arquitetura para um ambiente de grade computacional direcionado às aplicações distribuídas, móveis e conscientes do contexto da computação pervasiva , 2004 .

[26]  Stathes Hadjiefthymiades,et al.  An adaptive location prediction model based on fuzzy control , 2011, Comput. Commun..

[27]  Benţa Kuderna-Iulian,et al.  Towards an Affective Aware Home , 2009, ICOST 2009.

[28]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[29]  Mica R. Endsley,et al.  Expertise and Situation Awareness , 2006 .

[30]  Hans W. Guesgen,et al.  Spatial and Temporal Reasoning , 2003, AI Commun..

[31]  Marcel Cremene,et al.  Training the Behaviour Preferences on Context Changes , 2010, Electron. Commun. Eur. Assoc. Softw. Sci. Technol..

[32]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[33]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[34]  Jadwiga Indulska,et al.  A survey of context modelling and reasoning techniques , 2010, Pervasive Mob. Comput..