In the context of sequence processing, we study the relationship between single-layer feedforward neural networks, that have simultaneous access to all items composing a sequence, and single-layer recurrent neural networks which access information one step at a time. We treat both linear and nonlinear networks, describing a constructive procedure, based on linear autoencoders for sequences, tha...

Electroencephalography (EEG) is a complex voltage signal of the brain and its correct interpretation requires years training. Modern machine- learning methods help us to extract information from EEG recordings therefore several brain-computer interface (BCI) systems use them in clinical applications. By processing publicly available PhysioNet dataset, we extracted that could be used for trainin...

Heat transfer fluids have inherently low thermal conductivity that greatly limits the heat exchange efficiency. While the effectiveness of extending surfaces and redesigning heat exchange equipments to increase the heat transfer rate has reached a limit, many research activities have been carried out attempting to improve the thermal transport properties of the fluids by adding more thermally c...

This paper describes a neural network based controller for allocating capacity in a telecommunications network. This system was proposed in order to overcome a "real time" response constraint. Two basic architectures are evaluated: 1) a feedforward network-heuristic and; 2) a feedforward network-recurrent network. These architectures are compared against a linear programming (LP) optimiser as a...

This paper discusses the notion of generalization of training samples over long distances in the input space of a feedforward neural network. Such a generalization might occur in various ways, that di er in how great the contribution of di erent training features should be. The structure of a neuron in a feedforward neural network is analyzed and it is concluded, that the actual performance of ...

The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence ...

In this paper, we implement the method of Steepest Descent in single and multilayer feedforward artificial neural networks. In all previous works, all the update weight equations for single or multilayer feedforward artificial neural networks has been calculated by choosing a single activation function for various processing unit in the network. We, at first, calculate the total error function ...

This paper studies how the generalization ability of neurons can be affected by mutual processing of different signals. This study is done on the basis of a feedforward artificial neural network, that is used here as a model of the very basic processes in a network of biological neurons. The mutual processing of signals, called here an interference of signals, can possibly be a good model of pa...