Concept

Perceptrons (book)

Publications associées (69)

High Order and Multilayer Perceptron Initialization

Proper initialization is one of the most important prerequisites for fast convergence of feed-forward neural networks like high order and multilayer perceptrons. This publication aims at determining the optimal variance (or range) for the initial weights a ...

IEEE1997

Handwritten Digit Recognition with Binary Optical Perceptron

Binary weights are favored in electronic and optical hardware implementations of neural networks as they lead to improved system speeds. Optical neural networks based on fast ferroelectric liquid crystal binary level devices can benefit from the many order ...

Springer-Verlag1997

Handwritten Digit Recognition with Binary Optical Perceptron

IDIAP1997

Optimization of high order perceptrons

Neural networks are widely applied in research and industry. However, their broader application is hampered by various technical details. Among these details are several training parameters and the choice of the topology of the network. The subject of this ...

École Polytechnique Fédérale de Lausanne1997

Optimization of high order perceptrons

EPFL1997

Bounds on the Degree of High Order Binary Perceptrons

High order perceptrons are often used in order to reduce the size of neural networks. The complexity of the architecture of a usual multilayer network is then turned into the complexity of the functions performed by each high order unit and in particular b ...

D facto1996

A Method for All-Positive Optical Multilayer Perceptrons

The most promising approaches for optical neural networks are based on intensity encoding. However, a serious drawback of intensity encoding is the lack of negative values and optical subtraction, which are essential for rendering neural networks useful. T ...

IEEE1996

On the Robustness of Perceptron Learning Recurrent Networks

Ali H. Sayed

This paper extends a recent time-domain feedback analysis of Perceptron learning networks to recurrent networks and provides a study of the robustness performance of the training phase in the presence of uncertainties. In particular. a bound is established ...

Elsevier1996

Heterogeneous artificial neural network for short term electrical load forecasting

Short-term electrical load forecasting is a topic of major interest for the planning of energy production and distribution. The use of artificial neural networks has been demonstrated as a valid alternative to classical statistical methods in term of accur ...

1996

Constructive Training Methods for Feedforward Neural Networks with Binary Weights

Frédéric Jean-Michel Aviolat

Quantization of the parameters of a Perceptron is a central problem in hardware implementation of neural networks using a numerical technology. A neural model with each weight limited to a small integer range will require little surface of silicon. Moreove ...

1996

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