A neural network PID autotuner

Proportional, Integral and Derivative (PID) regulators are standard building blocks for industrial automation. Their popularity comes from their rebust performance and also from their functional simplicity. Whether because the plant is time-varying, or because of components ageing, these controllers need to be regularly retuned.

A neural network controller

Proportional, Integral and Derivative (PID) regulators are standard building blocks for industrial automation. The popularity of these regulators comes from their rebust performance in a wide range of operating conditions, and also from their functional simplicity, which makes them suitable for manual tuning.

A new formulation of the learning problem for a neural network controller

In this paper we consider the learning problem for a class of multilayer perceptrons which is practically relevant in control systems applications. By reformulating this problem, a new criterion is developed, which reduces the number of iterations required for the learning phase.

Supervised training approach using spiking neural networks

One of the basic aspects of some neural networks is their attempt to approximate as much as possible their biological counterparts. The goal is to achieve a simple and robust network, easy to understand and able of simulating the human brain at a computational level. Recently a third generation of neural networks (NN) [1], called Spiking Neural Networks(SNN) was appeared. This new kind of networks use the time of a electrical pulse, or spike, to encode the information. In the first and second generation of NN analog values are used in the communication between neurons.

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

In this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.

Predicting the greenhouse inside air temperature with RBF neural networks

The application of the Radial Basis Function (RBF) Neural Network (NN) to greenhouse inside air temperature modelling has been previously investigated (Ferreira et al., 2000a). In those studies, the inside air temperature is modelled as a function of the inside relative humidity and of the outside temperature and solar radiation. A second-order model structure previously selected (Cunha et al., 1996) in the context of dynamic temperature models identification, is used.

Face recogniton based on the optimal combination of neural networks, eigenfaces and least squares matching methods

Report of a research project of the Fachhochschule Hannover, University of Applied Sciences and Arts, Department of Information Technologies. Automatic face recognition increases the security standards at public places and border checkpoints. The picture inside the identification documents could widely differ from the face, that is scanned under random lighting conditions and for unknown poses. The paper describes an optimal combination of three key algorithms of object recognition, that are able to perform in real time. The camera scan is processed by a recurrent neural network, by a Eigenfaces (PCA) method and by a least squares matching algorithm. Several examples demonstrate the achieved robustness and high recognition rate.

Nuclear and mitochondrial control of the apoptosis machinery regulates neural stem cell fate

Tese de doutoramento, Farmácia (Bioquímica), Universidade de Lisboa, Faculdade de Farmácia, 2014

'Team GB', 'Team England' or 'Team Scotland'? Media Representations of 'Britishness', 'Englishness' and 'Scottishness' in London 2012 and Glasgow 2014

Given the timing of the Scottish independence referendum in September 2014, the hosting of both the London 2012 Olympic Games and the Glasgow 2014 Commonwealth Games coincided with a period of considerable political turmoil and reflection within the United Kingdom. The extensive levels of public, political and media scrutiny of both of these major sporting events can therefore be framed within a wider consideration of the contemporary dynamics of the political union between the constituent nations of the UK, as well as the multifarious forms of national identities expressed within the various regions of the ‘nation-state’. Despite the growing influence of social media forms within contemporary society, politics and sport within the UK, the ‘traditional’ print media retain a central (although arguably diminishing) role in the dissemination of information relating to major societal, political and sporting issues to the British public. This paper will therefore critically reflect upon the nature of print media representations of ‘Britishness’, ‘Englishness’ and ‘Scottishness’ at London 2012 and Glasgow 2014 from both London-based and Scotland-based publications, drawing upon empirical data from completed and ongoing doctoral theses from the respective authors. In particular, the implications of the contrasting competitive structures of each event will be considered, given the symbolic differences between the unitary ‘Team GB’ at the London 2012 Olympics and the separated representative teams for Scotland and England at the Glasgow 2014 Commonwealth Games.

Exploring the modulatory role of A2A receptors in oligodendrogenesis derived from neural stem/progenitor cells of the subventricular zone

Tese de mestrado, Neurociências, Faculdade de Medicina, Universidade de Lisboa, 2015

Identification of meat spoilage by FTIR spectroscopy and neural networks

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.