Specification And Implementation Of A Digital Hopfield-Type Associative Memory With On-Chip Training

This paper addresses the definition of the requirements for the design of a neural network associative memory, with on-chip training, in standard digital CMOS technology. We investigate various learning rules which are integrable in silicon, and we study the associative memory properties of the resulting networks. We also investigate the relationships between the architecture of the circuit and the learning rule, in order to minimize the extra circuitry required for the implementation of training. We describe a 64neuron associative memory with on-chip training, which has been manufactured, and we outline its future extensions. Beyond the application to the specific circuit described in the paper, the general methodology for determining the accuracy requirements can be applied to other circuits and to other auto-associative memory architectures. 1. INTRODUCTION The present paper describes the specification and the silicon integration of a Hopfield neural network designed (i) to operate ..

Ressources en eau à l’échelle des bassins versants

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Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France)

International audienceIn southern France, flash flood episodes frequently cause fatalities and severe damage. In order to inform and warn populations, the French flood forecasting service (SCHAPI, Service Central d'Hydrométéorologie et d'Appuià la Prévision des Inondations) initiated the BVNE (Bassin Versant Numérique Expérimental, or Experimental Digital Basin) project in an effort to enhance flash flood predictability. The target area for this study is the Gardon d'Anduze basin, located in the heart of the Cévennes range. In this Mediterranean mountainous setting, rainfall intensity can be very high, resulting in flash flooding. Discharge and rainfall gauges are often exposed to extreme weather conditions , which undermines measurement accuracy and continuity. Moreover, the processes governing rainfall-discharge relations are not well understood for these steeply-sloped and heterogeneous basins. In this context of inadequate information on both the forcing variables and process knowledge , neural networks are investigated due to their universal approximation and parsimony properties. We demonstrate herein that thanks to a rigorous variable and complexity selection , efficient forecasting of up to two-hour durations, without requiring rainfall forecasting as input, can be derived using the measured discharges available from a feedforward model. In the case of discharge gauge malfunction, in degraded mode, forecasting may result using a recurrent neural network model. We also observe that neural network models exhibit low sensitivity to uncertainty in rainfall measurements since producing ensemble forecasting does not significantly affect forecasting quality. In providing good results, this study suggests close consideration of our main purpose: generating forecasting on ungauged basins

Feed-forward vs recurrent neural network models for non-stationarity modelling using data assimilation and adaptivity

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Flash Floods Forecasting in a Karstic Basin Using Neural Networks: the Case of the Lez Basin (South of France)

International audienceThe present study focuses on the modeling of the Lez karstic system (France) using artificial neural networks. Two methods of variable selection were compared: cross-correlation and cross-validation. In both cases, the artificial neural network forecasts closely matched the measured discharge, giving Nash criteria higher than 0.8, which can thus provide satisfactory 2-day forecasts

Ensemble model to enhance robustness of flash flood forecasting using an Artificial Neural Network: case-study on the Gardon Basin (south-eastern France)

International audienceDuring the last few decades neural networks have been increasingly used in hydrological modelling for theirfundamental property of parsimony and of universal approximation of non-linear functions. For the purposeof flash flood forecasting, feed-forward and recurrent multi-layer perceptrons appear to be efficient tools.Nevertheless, their forecasting performances are sensitive to the initialization of the network parameters. Wehave studied the cross-validation efficiency to select initialization providing the best forecasts in real time situation.Sensitivity to initialization of feed-forward and recurrent models is compared for one-hour lead-timeforecasts. This study shows that cross-validation is unable to select the best initialization. A more robustmodel has been designed using the median of several models outputs; in this context, this paper analysesthe design of the ensemble model for both recurrent and feed-forward models

Neural Networks for Karst Spring Management. Case of the Lez Spring (Southern France)

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