Journal ArticleDOI
2008 Special Issue: Event detection and localization for small mobile robots using reservoir computing
Eric Aislan Antonelo,Benjamin Schrauwen,Dirk Stroobandt +2 more
- Vol. 21, Iss: 6, pp 862-871
TLDR
Reservoir Computing techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods.Abstract:
Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Experimental demonstration of reservoir computing on a silicon photonics chip
Kristof Vandoorne,Pauline Mechet,Thomas Van Vaerenbergh,Martin Fiers,Geert Morthier,David Verstraeten,Benjamin Schrauwen,Joni Dambre,Peter Bienstman +8 more
TL;DR: This work proposes the first integrated passive silicon photonics reservoir and demonstrates that this generic chip can be used to perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s(-1), without power consumption in the reservoir.
Journal ArticleDOI
High-performance photonic reservoir computer based on a coherently driven passive cavity
Quentin Vinckier,Francois Duport,Anteo Smerieri,Kristof Vandoorne,Peter Bienstman,Marc Haelterman,Serge Massar +6 more
TL;DR: In this paper, a photonic implementation of a reservoir computer based on a coherently driven passive fiber cavity processing analog signals is presented, which has error rate as low as or lower than previous experiments on a wide variety of tasks, and also has lower power consumption.
Journal ArticleDOI
Fully analogue photonic reservoir computer.
TL;DR: This work addresses the possibility to get rid simultaneously of both digital pre and post processing in photonic reservoir computing, and constitutes a proof of concept for standalone physical reservoir computers.
Journal ArticleDOI
An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation
TL;DR: An integrated prognostic approach that unifies two types of health indices, battery capacity and time interval of equal discharging voltage difference series, to perform direct and indirect RUL estimation for lithium-ion battery is presented.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Book
Pattern Recognition and Machine Learning (Information Science and Statistics)
TL;DR: Looking for competent reading resources?
Journal ArticleDOI
The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat
TL;DR: Preliminary observations on the behaviour of hippocampusal units in the freely moving rat provide support for this theory of hippocampal function.
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