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A neural network architecture, subjected to incon-gruent stimuli in the form of lip reading of spoken syllables and listening to different spoken syllables, is shown to generate the well-known McGurk effect, e.g. visual /ga/ and auditory /ba/ is perceived as /da/ by the network. The neural network is based on an architecture which has previously been successfully applied to sensory integration of...
In this paper, an event-based near optimal control of uncertain nonlinear discrete time systems is presented by using input-output data and approximate dynamic programming (ADP). The nonlinear system dynamics in affine form are transformed into an input-output form. Then, three neural networks (NN) with event sampled input-output vector are used, namely, the identifier NN to relax the knowledge of...
Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models. In this work we show that Computational Intelligence can offer a more holistic approach that is more suitable for the complex relationships an indebtness dataset has and...
Recent studies have shown that memristor crossbar based neuromorphic hardware enables high performance implementations of neural networks at low power and in low chip area. This paper presents circuits to train a cascaded set of memristor crossbars representing a multi-layered neural network. The circuits presented implement back-propagation training and would enable on-chip training of memristor...
In this paper a comparison between an ensembles (multi-classifier) constructed of several machine learning methods (support vector machine, artificial neural network, naive Bayesian classifier, decision trees, radial basis function and k nearest neighbors) versus each single classifiers of these methods in term of gold mine underground dam levels prediction is presented. The ensembles as well as the...
This paper presents an experiment that consists of constructing auto-regressive moving average (ARMA), neural networks and neuro-fuzzy models with historical electricity consumption time series data to create models that can be used to forecast consumption in the future. The data was sampled on a monthly basis from January 1985 to December 2011. An ARMA, multilayer perceptron neural network with back...
We investigate further the problem of radar signal classification and source identification with neural networks. The available large dataset includes pulse train characteristics such as signal frequencies, type of modulation, pulse repetition intervals, scanning type, scan period, etc., represented as a mixture of continuous, discrete and categorical data. Typically, considerable part of the data...
Humans learn in an incremental manner. Due to this reason, humans continuously refine their knowledge of the environment with the experience gained. Many strides have been made in the machine learning area to exploit the power of incremental learning. Incremental learning, in contrast to onetime learning is far more useful and effective when data is not completely available at once. Here, we investigate...
In this paper, a novel learning method is introduced that borrows simultaneously from the principles of kernel methods and multi-layer perceptron. Specifically, the method implements the feature mapping idea of kernel methods into a multi-layer perceptron. Unlike in kernel learning where the feature space is usually invisible and inaccessible, the multilayer perceptron based mapping is explicit. Therefore,...
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