In this work we present the cooperative coevolution of multi-layer generalized perceptrons. This model is based on the cooperation of different subpopulations of modules, each one being a generalized multi-layered perceptron.In some previous works we have developed a modular cooperative...

A multilayer neural network with one hidden layer and with 1 hidden layer neurons can exactly implement an arbitrary training set with training patterns. It is only sufficient and not necessary to use -1 hidden neurons. For a particular training set, this number can be reduced. Here, monotone...

This paper aims to establish a patient's intracranial pressure (ICP) model in neurosurgical intensive care unit using neural network. Non-invasive physiological signals from patients including mean arterial pressure (MAP), heart rate (HR), end-tidal of carbon dioxide (EtCO2) and regional...

A new multi-layer artificial neural network learning algorithm based on pattern search method is proposed. The learning model is designed to provide a very simple and effective means of searching the minima of objective function of a multi-layer neural network directly without any knowledge of...

Nitrendipine is an effective and safe calcium channel blocker for the treatment of mild to moderate hypertension. The aim of this work is to show that a model of a relation between nitrendipine plasma levels and pharmacodynamic effects can be composed and used for pressure drop prediction after...

It is well-known that abstract neurons of the McCulloch-Pitts type compute input-output functions that are formally equivalent to suitably defined Boolean functions. Boolean functions, in turn, can be classified (up to isomorphism of underlying logical structure) into a limited number of basic...

In this paper we propose a neural network architecture that is capable of continuous learning multiple, possibly overlapping, arbitrary input sequences relatively quickly, autonomously and online. The architecture of this network has been constructed according to design principles derived from...

We extend the approach to learning a topological description of the environment with recurrent neural networks. Usually, a predetermined reactive behavior and a predefined criterion for decision points are used. In our extended approach, both the reactive behavior and the criterion for the...

Backpropagation with selective training (BST) is applied on training RBF networks. It improves the performance of the RBF network substantially, in terms of convergence speed and recognition error. Three drawbacks of the basic backpropagation algorithm, i.e. overtraining, slow convergence at the...

Feed-forward networks are generally trained to represent functions or many-to-one (m-o) mappings. In this paper however a feed-forward network with modified training algorithm is trained to represent multi-valued or one-to-many (o-m) mappings. The o-m mapping is replaced by a m-o mapping with...