ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Classificação e previsão de cardiotocogramas usando algoritmos baseados em redes neuronais

Mestrado em Computação e Instrumentação Médica

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Exciting dynamical behavior in a network of two coupled rings of Chen oscillators

We study exotic patterns appearing in a network of coupled Chen oscillators. Namely, we consider a network of two rings coupled through a “buffer” cell, with Z3×Z5 symmetry group. Numerical simulations of the network reveal steady states, rotating waves in one ring and quasiperiodic behavior in the other, and chaotic states in the two rings, to name a few. The different patterns seem to arise through a sequence of Hopf bifurcations, period-doubling, and halving-period bifurcations. The network architecture seems to explain certain observed features, such as equilibria and the rotating waves, whereas the properties of the chaotic oscillator may explain others, such as the quasiperiodic and chaotic states. We use XPPAUT and MATLAB to compute numerically the relevant states.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Strange Dynamics in a Fractional Derivative of Complex-Order Network of Chaotic Oscillators

We study the peculiar dynamical features of a fractional derivative of complex-order network. The network is composed of two unidirectional rings of cells, coupled through a "buffer" cell. The network has a Z3 × Z5 cyclic symmetry group. The complex derivative Dα±jβ, with α, β ∈ R+ is a generalization of the concept of integer order derivative, where α = 1, β = 0. Each cell is modeled by the Chen oscillator. Numerical simulations of the coupled cell system associated with the network expose patterns such as equilibria, periodic orbits, relaxation oscillations, quasiperiodic motion, and chaos, in one or in two rings of cells. In addition, fixing β = 0.8, we perceive differences in the qualitative behavior of the system, as the parameter c ∈ [13, 24] of the Chen oscillator and/or the real part of the fractional derivative, α ∈ {0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, are varied. Some patterns produced by the coupled system are constrained by the network architecture, but other features are only understood in the light of the internal dynamics of each cell, in this case, the Chen oscillator. What is more important, architecture and/or internal dynamics?

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Identifying the neural correlates of executive functions in early cerebral microangiopathy: a combined VBM and DTI study.

Cerebral microangiopathy (CMA) has been associated with executive dysfunction and fronto-parietal neural network disruption. Advances in magnetic resonance imaging allow more detailed analyses of gray (e.g., voxel-based morphometry-VBM) and white matter (e.g., diffusion tensor imaging-DTI) than traditional visual rating scales. The current study investigated patients with early CMA and healthy control subjects with all three approaches. Neuropsychological assessment focused on executive functions, the cognitive domain most discussed in CMA. The DTI and age-related white matter changes rating scales revealed convergent results showing widespread white matter changes in early CMA. Correlations were found in frontal and parietal areas exclusively with speeded, but not with speed-corrected executive measures. The VBM analyses showed reduced gray matter in frontal areas. All three approaches confirmed the hypothesized fronto-parietal network disruption in early CMA. Innovative methods (DTI) converged with results from conventional methods (visual rating) while allowing greater spatial and tissue accuracy. They are thus valid additions to the analysis of neural correlates of cognitive dysfunction. We found a clear distinction between speeded and nonspeeded executive measures in relationship to imaging parameters. Cognitive slowing is related to disease severity in early CMA and therefore important for early diagnostics.

Behavioural and neural correlates of operant conditioning in Lymnaea stagnalis : role of previous experience during development /

The freshwater mollusc Lymnaea stagnalis was utilized in this study to further the understanding of how network properties change as a result of associative learning, and to determine whether or not this plasticity is dependent on previous experience during development. The respiratory and neural correlates of operant conditioning were first determined in normally reared Lymnaea. The same procedure was then applied to differentially reared Lymnaea, that is, animals that had never experienced aerial respiration during their development. The aim was to determine whether these animals would demonstrate the same responses to the training paradigm. In normally reared animals, a behavioural reduction in aerial respiration was accompanied by numerous changes within the neural network. Specifically, I provide evidence of changes at the level of the respiratory central pattern generator and the motor output. In the differentially reared animals, there was little behavioural data to suggest learning and memory. There were, however, significant differences in the network parameters, similar to those observed in normally reared animals. This demonstrated an effect of operant conditioning on differentially reared animals. In this thesis, I have identified additional correlates of operant conditioning in normally reared animals and provide evidence of associative learning in differentially reared animals. I conclude plasticity is not dependent on previous experience, but is rather ontogenetically programmed within the neural network.

Génération et reconnaissance de rythmes au moyen de réseaux de neurones à réservoir

Les fichiers sons qui accompagne mon document sont au format midi. Le programme que nous avons développés pour ce travail est en language Python.

Étude de techniques d'apprentissage non-supervisé pour l'amélioration de l'entraînement supervisé de modèles connexionnistes

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Algorithmes d'apprentissage pour la recommandation

L'ère numérique dans laquelle nous sommes entrés apporte une quantité importante de nouveaux défis à relever dans une multitude de domaines. Le traitement automatique de l'abondante information à notre disposition est l'un de ces défis, et nous allons ici nous pencher sur des méthodes et techniques adaptées au filtrage et à la recommandation à l'utilisateur d'articles adaptés à ses goûts, dans le contexte particulier et sans précédent notable du jeu vidéo multi-joueurs en ligne. Notre objectif est de prédire l'appréciation des niveaux par les joueurs. Au moyen d'algorithmes d'apprentissage machine modernes tels que les réseaux de neurones profonds avec pré-entrainement non-supervisé, que nous décrivons après une introduction aux concepts nécessaires à leur bonne compréhension, nous proposons deux architectures aux caractéristiques différentes bien que basées sur ce même concept d'apprentissage profond. La première est un réseau de neurones multi-couches pour lequel nous tentons d'expliquer les performances variables que nous rapportons sur les expériences menées pour diverses variations de profondeur, d'heuristique d'entraînement, et des méthodes de pré-entraînement non-supervisé simple, débruitant et contractant. Pour la seconde architecture, nous nous inspirons des modèles à énergie et proposons de même une explication des résultats obtenus, variables eux aussi. Enfin, nous décrivons une première tentative fructueuse d'amélioration de cette seconde architecture au moyen d'un fine-tuning supervisé succédant le pré-entrainement, puis une seconde tentative où ce fine-tuning est fait au moyen d'un critère d'entraînement semi-supervisé multi-tâches. Nos expériences montrent des performances prometteuses, notament avec l'architecture inspirée des modèles à énergie, justifiant du moins l'utilisation d'algorithmes d'apprentissage profonds pour résoudre le problème de la recommandation.

Neural changes associated with semantic processing in healthy aging despite intact behavioral performance

Semantic memory recruits an extensive neural network including the left inferior prefrontal cortex (IPC) and the left temporoparietal region, which are involved in semantic control processes, as well as the anterior temporal lobe region (ATL) which is considered to be involved in processing semantic information at a central level. However, little is known about the underlying neuronal integrity of the semantic network in normal aging. Young and older healthy adults carried out a semantic judgment task while their cortical activity was recorded using magnetoencephalography (MEG). Despite equivalent behavioral performance, young adults activated the left IPC to a greater extent than older adults, while the latter group recruited the temporoparietal region bilaterally and the left ATL to a greater extent than younger adults. Results indicate that significant neuronal changes occur in normal aging, mainly in regions underlying semantic control processes, despite an apparent stability in performance at the behavioral level.

Identification of spectral lines of elements using artificial neural networks

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. This paper describes how an ANN can be used to identify the spectral lines of elements. The spectral lines of Cadmium (Cd), Calcium (Ca), Iron (Fe), Lithium (Li), Mercury (Hg), Potassium (K) and Strontium (Sr) in the visible range are chosen for the investigation. One of the unique features of this technique is that it uses the whole spectrum in the visible range instead of individual spectral lines. The spectrum of a sample taken with a spectrometer contains both original peaks and spurious peaks. It is a tedious task to identify these peaks to determine the elements present in the sample. ANNs capability of retrieving original data from noisy spectrum is also explored in this paper. The importance of the need of sufficient data for training ANNs to get accurate results is also emphasized. Two networks are examined: one trained in all spectral lines and other with the persistent lines only. The network trained in all spectral lines is found to be superior in analyzing the spectrum even in a noisy environment.