Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Decision Making in Evolving Artificial Systems
2001
The theme of this workshop is artificial perception. In this chapter we will argue that the ecological function of perception is to serve decision-making. If this is so the mechanisms chosen to implement perception, in natural or artificial systems, will be constrained by the requirements of decision-making and theories of decision-making will inevitably influence theories of perception. In what follows we will look at decision-making from what we hope is a new perspective, applying concepts and techniques developed by what we will call “new artificial intelligence”. We will begin, in the second part of the chapter, with a review of traditional, “normative” theories of decision-making and o…
Analysis of Game Creativity Development by Means of Continuously Learning Neural Networks
2006
Experts in ball games are characterized by extraordinary creative behavior. This article outlines a framework of analyzing creative performance based on neural networks. The aim of this study is to compare the potential of different kinds of training programs with the learning of game creativity in real field contexts. The training groups (soccer group, n=20; field hockey group, n=17) showed significant improvement in comparison to the control group (n=18) with respect to the three measuring points, although no difference could be established between the groups. As regards the development of performance, five types of learning behavior can be distinguished, the most striking ones being what…
Soccer analyses by means of artificial neural networks, automatic pass recognition and Voronoi-cells: An approach of measuring tactical success.
2015
Success in a soccer match is usually measured by goals. However, in order to yield goals, successful tactical pre-processing is necessary. If analyzing a match with the focus on “success”, promising tactical activities including vertical passes with control win in the opponent’s penalty area have to be the focus. Whether or not a pass is able to crack the opponent’s defence depends on the tactical formations of both the opponent’s defence and the own offence group.
Towards a model-based cognitive neuroscience of stopping - a neuroimaging perspective.
2018
Our understanding of the neural correlates of response inhibition has greatly advanced over the last decade. Nevertheless the specific function of regions within this stopping network remains controversial. The traditional neuroimaging approach cannot capture many processes affecting stopping performance. Despite the shortcomings of the traditional neuroimaging approach and a great progress in mathematical and computational models of stopping, model-based cognitive neuroscience approaches in human neuroimaging studies are largely lacking. To foster model-based approaches to ultimately gain a deeper understanding of the neural signature of stopping, we outline the most prominent models of re…
A nonlinear electronic circuit mimicking the neuronal activity in presence of noise
2013
We propose a nonlinear electronic circuit simulating the neuronal activity in a noisy environment. This electronic circuit is ruled by the set of Bonhaeffer-Van der Pol equations and is excited with a white gaussian noise, that is without external deterministic stimuli. Under these conditions, our circuits reveals the Coherence Resonance signature, that is an optimum of regularity in the system response for a given noise intensity.
A neural network based automatic road signs recognizer
2003
Automatic road sign recognition systems are aimed at detection and recognition of one or more road signs from real-world color images. In this research, road signs are detected and extracted from real world scenes on the basis of their color and shape features. A dynamic region growing technique is adopted to enhance color segmentation results obtained in the HSV color space. The technique is based on a dynamic threshold that reduces the effect of hue instability in real scenes due to external brightness variation. Classification is then performed on extracted candidate regions using multilayer perceptron neural networks. The obtained results show good detection and recognition rates of the…
Best not to bet on the horserace: A comment on Forrin and MacLeod (2017) and a relevant stimulus-response compatibility view of colour-word contingen…
2018
International audience; One powerfully robust method for the study of human contingency learning is the colour-word contingency learning paradigm. In this task, participants respond to the print colour of neutral words, each of which is presented most often in one colour. The contingencies between words and colours are learned, as indicated by faster and more accurate responses when words are presented in their expected colour relative to an unexpected colour. In a recent report, Forrin and MacLeod (2017b, Memory & Cognition) asked to what extent this performance (i.e., response time) measure of learning might depend on the relative speed of processing of the word and the colour. With keypr…
Identifying individuality and variability in team tactics by means of statistical shape analysis and multilayer perceptrons.
2012
Abstract Offensive and defensive systems of play represent important aspects of team sports. They include the players’ positions at certain situations during a match, i.e., when players have to be on specific positions on the court. Patterns of play emerge based on the formations of the players on the court. Recognition of these patterns is important to react adequately and to adjust own strategies to the opponent. Furthermore, the ability to apply variable patterns of play seems to be promising since they make it harder for the opponent to adjust. The purpose of this study is to identify different team tactical patterns in volleyball and to analyze differences in variability. Overall 120 s…
Digital information receiver based on stochastic resonance
2003
International audience; An electronic receiver based on stochastic resonance is presented to rescue subthreshold modulated digital data. In real experiment, it is shown that a complete data restoration is achieved for both uniform and Gaussian white noise.
Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles
2016
Solar photovoltaic plants power output forecasting using machine learning techniques can be of a great advantage to energy producers when they are implemented with day-ahead energy market data. In this work a model was developed using a supervised learning algorithm of multilayer perceptron feedforward artificial neural network to predict the next twenty-four hours (day-ahead) power of a solar facility using fetched weather forecast of the following day. Each set of tested network configuration was trained by the historical power output of the plant as a target. For each configuration, one hundred networks ensembles was averaged to give the ability to generalize a better forecast. The train…