Event detection and localization for small mobile robots using reservoir computing

Antonelo, Eric Aislan; Schrauwen, Benjamin; Stroobandt, Dirk

doi:10.1016/j.neunet.2008.06.010

Cited by 77 publications

(50 citation statements)

References 19 publications

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“…Without the trained feedback, any continuous problem that requires only fading memory (a broad class of realworld problems) can be solved under some general and mild conditions 2 . The popularity of RC stems from its ease of use, combined with its computational capabilities that match or exceed the state-of-the-art for a broad range of applications such as speech recognition, time series prediction, pattern classification and robotics 1,[7][8][9][10] . Its lenient requirements for the reservoir have led to implementations on several hardware platforms ranging from a basin of water to cellular neural networks and bacteria [11][12][13] .…”

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confidence: 99%

Experimental demonstration of reservoir computing on a silicon photonics chip

et al. 2014

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In today's age, companies employ machine learning to extract information from large quantities of data. One of those techniques, reservoir computing (RC), is a decade old and has achieved state-of-the-art performance for processing sequential data. Dedicated hardware realizations of RC could enable speed gains and power savings. Here we propose the first integrated passive silicon photonics reservoir. We demonstrate experimentally and through simulations that, thanks to the RC paradigm, 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 À 1 , without power consumption in the reservoir. It can also perform isolated spoken digit recognition. Our realization exploits optical phase for computing. It is scalable to larger networks and much higher bitrates, up to speeds 4100 Gbit s À 1 . These results pave the way for the application of integrated photonic RC for a wide range of applications.

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confidence: 99%

Experimental demonstration of reservoir computing on a silicon photonics chip

et al. 2014

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“…This layer yields a state space sensitive to signals working at different timescales. A similar approach was shown in [7], where a single reservoir with multiple leak rates for individual neurons yielded better performance in robot localization tasks than using only one leak rate. The second layer (PCA) learns the principal components of the previous Res.1 layer by finding a linear projection from a high-dimensional reservoir space into a low dimension orthogonal space.…”

Section: Introductionmentioning

confidence: 78%

Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells

Antonelo¹,

Camponogara²,

Foss

2017

Neural Networks

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Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESN) as an efficient technique for training Recurrent Neural Networks. We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that the latter architecture performs better in modeling both large irregular transients and more commonly occurring small oscillations.

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“…In previous work, RC has already proven its capabilities in a broad range of applications including robot localization [10], chaotic time series prediction [11] and speech recognition [12]. Additionally, researchers are making efforts to directly implement such systems on hardware [13].…”

Section: Reservoir Computingmentioning

confidence: 99%

Towards a Neural Hierarchy of Time Scales for Motor Control

Waegeman

wyffels

Schrauwen

2012

From Animals to Animats 12

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Abstract. Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control.

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Event detection and localization for small mobile robots using reservoir computing

Cited by 77 publications

References 19 publications

Experimental demonstration of reservoir computing on a silicon photonics chip

Experimental demonstration of reservoir computing on a silicon photonics chip

Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells

Towards a Neural Hierarchy of Time Scales for Motor Control

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