1,721,118 research outputs found
A 'combination of multiple classifier' design for low-complex, highly performing and power-aware classifiers
No full textIn this paper we study the relationships among the Combination of Multiple Classifier design philosophy, application level properties such as temporal and spatial locality of the inputs and low level aspects immediately impacting on power consumption, cache miss and computational complexity reduction. The CMC structure requires a set of independent simple sub-classifiers, each of which ruling in an application sub-domain under the control of a master enabling module and is particularly appealing in embedded system implementation. Only a sub-classifier is active at a time, the others being switched off
Guest Editorial - Special issue on neural technologies for identification, control, robotics, and signal/image processing
No full textGuest editorial - Special issue on neural technologies for identification, control, robotics, and signal/image processing
No full textNeural modeling of dynamic systems with non-measurable state variables
No full textThe paper studies the ability possessed by recurrent neural networks to model dynamic systems when some relevant state variables are not measurable. Neural architectures based on virtual states - which naturally arise from a space state representation - are introduced and compared with the more traditional neural output error ones. Despite the evident potential model ability possessed by virtual state architectures we experimented that their performances strongly depend on the training efficiency. A novel validation criterion for neural output error architectures is suggested which allows to assess the neural network not only in terms of its approximation accuracy but also with respect to stability issues
Experimental neural networks for prediction and identification
No full textIn this paper we prove the effectiveness of using simple NARX-type (nonlinear auto-regressive model with exogenous variables) recurrent neural networks to identify time series and nonlinear dynamical systems. Experimentally we show that, whenever the process generating the data is ruled by a linear model, the performances provided by the neural network are comparable with the ones given by the optimal predictor determined according to the Kolmogorov-Wiener theory. On the other hand, whenever the system to be modelled is intrinsically nonlinear, its performance approaches that obtainable with classical linear identification. The work extends that suggested by Narendra in (1990) by considering a reduced set of training data and a black-box model for the system to be identified
Sensitivity to errors in artificial neural networks : a behavioral approach
No full textThe problem of sensitivity to errors in artificial neural networks is discussed here considering an abstract model of the network and the errors that can affect a neuron's computation. Feed-forward multilayered networks are considered; the performance taken into account with respect to error sensitivity is their classification capacity. The final aim is evaluation of the probability that a single neuron's error will affect both its own classification capacity and that of the whole network. A geometrical representation of the neural computation is adopted as the basis for such evaluation. Probability of error propagation is evaluated with respect to the single neuron's output as well as to the complete network's output. The information derived is used to evaluate, for a specific digital network architecture, the most critical sections of the implementation as far as reliability is concerned and thus to point out candidates for ad-hoc fault-tolerance policies
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