Surrogate solutions of Fredholm integral equations by feedforward neural networks are investigated theoretically. Convergence of surrogate solutions computable by networks with increasing numbers of computational units to theoretically optimal solutions is proven and upper bounds on rates of convergence are derived. The results hold for a variety of computational units, they are illustrated by ...

This paper presents some apprilcations of neural networks in the microwave modeling. The applications are related to modeling of either passive or active structures and devices. Modeling is performed using not only simple multilayer perceptron network (MLP) but also advanced knowledge based neural network (KBNN) structures. Keywords–Neural network, modeling, microwave, microstrip gap, microwave...

Quantization of the parameters of a Perceptron is a central problem in hardware implementation of neural networks using a numerical technology. An interesting property of neural networks used as classiiers is their ability to provide some robustness on input noise. This paper presents eecient learning algorithms for the maximization of the robustness of a Perceptron and especially designed to t...

We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26% un...

This paper proposes a multilevel logic approach to output coding using multilevel neurons in the output layer. Training convergence for a single multilevel perceptron is considered. It has been found that a multilevel neural network classifier with a reduced number of outputs is often able to learn faster and requires fewer weights. Concepts are illustrated with an example of a digit classifier.

In this contribution we describe the modelling of the differential pressure behavior of isolation materials at a sieve by artificial neural networks (ANN). The subject arranges itself in the area of the reactor safety research. Compared with [3] the number of the inputs for the connection which can be modelled was increased. Thereby the number of necessary connections is reduced and the model q...

Metaplasticity property of biological synapses is interpreted in this paper as the concept of placing greater emphasis on training patterns that are less frequent. A novel implementation is proposed in which, during the network learning phase, a priority is given to weight updating of less frequent activations over the more frequent ones. Modeling this interpretation in the training phase, the ...

This paper investigates the credit scoring accuracy of "ve neural network models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance. The neural network credit scoring models are tested using 10-fold crossvalidation with two real world data sets. Results are benchmarked against more traditional methods under consideration...

In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a lear...