Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I David W. Pearson,Nigel C. Steele,Rudolf F. Albrech Conference proceedin

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Hybrid Models for Forecasting Air Pollution Episodes,g system for air pollution episodes is widely needed to minimize negative health effects. However the forecasting of air pollution episodes has been observed to be problematic partly due their rareness and short-term nature. The research presented here aims to evaluate different neural network based

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Vertical Vector Fields and Neural Networks: An Application in Atmospheric Pollution Forecasting,to determine zones in the input space that are mapped onto the same output, they act in a similar way to kernels of linear mappings but in a nonlinear setting. In the paper we illustrate our ideas using data from a real application, namely forecasting atmospheric pollution for the town of Saint-Etie

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mechanism

A hybrid algorithm for weight and connectivity optimization in feedforward neural networks,aining feedforward neural networks based on a genetic algorithm (GA), which simultaneously optimizes both weights and network connectivity structure. The proposed method has been found to yield dense and descriptive networks even from training sets of few observations.

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Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression,ors become the attractors of network dynamics, hence they can be revealed by the random search. The neurodynamics is modeled by Single-Step approximation which is known [5] to be rather accurate for sparsely encoded Hopfield-network. This paper is limited to the case of sparsely encoded factors, but it is not realy constraint for data compression.