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Publisher: IOS Press
Languages: English
Types: Unknown
Subjects:
Pattern analysis and prediction of sensory data is becoming an increasing scientific challenge and a massive economical interest supports the need for better pattern mining techniques. The aim of this paper is to investigate efficient mining of useful information from a sensor network representing an ambient intelligence environment. The goal is to extract and predict behavioral patterns of a person in his/her daily activities by analyzing the time series data representing the behaviour of the occupant, generated using occupancy sensors. There are various techniques available for analysis and prediction of a continuous time series signal. However, the occupancy signal is represented by a binary time series where only discrete values of a signal are available. To build the prediction model, recurrent neural networks are investigated. They are proven to be useful tools to solve the difficulties of the temporal relationships of inputs between observations at different time steps, by maintaining internal states that have memory. In this paper, a special form of recurrent neural network, the so-called Echo State Network (ESN) is used in which discrete values of time series can be well processed. Then, a model developed based on ESN is compared with the most popular recurrent neural net-works; namely Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL). The results showed that ESN provides better prediction results compared with BPTT and RTRL. Using ESN, large datasets are learnt in only few minutes or even seconds. It can be concluded that ESN are efficient and valuable tools in binary time series prediction. The results presented in this paper are based on simulated data generated from a simulator representing a person in a 1 bedroom flat.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Stipanciev D., Bodroti L. and Stula M., Environmental Itelligence Based on Advanced Sensor Networks, IEEE on systems, signals and image processing, 6th EURA SIP Conference, June (2007), pp. 209-212.
    • [2] Herbert Jaeger and Harald Haas, Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication, Appril, VOL 304, SCIENCE, (2004).
    • [3] Alexandre Devert, Nicolas Bredeche and Marc Schoenauer, Unsupervised Learning of Echo State Networks: A Case Study in Artificial Embryogeny, Artificial Evolution, Vol. 4926, (2008), pp. 278-290.
    • [4] Mark D. Skowronski and John G. Harris, Minimum mean squared error time series classification using an echo state network prediction model, IEEE International Symposium on Circuits and Systems, (2006).
    • [5] Zhiwei Shi and Min Han, Support Vector Echo-State Machine for Chaotic Time-Series Prediction,IEEE Transactions on Neural Networks, March, Vol. 18, Issue: 2, (2007), pp. 359-372.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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