Forecast of seasonal streamflow series with artificial neural networks and linear models adjusted for bio-inspired algorithms

Orientadores: Christiano Lyra Filho, Romis Ribeiro de Faissol AttuxDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: O Sistema Elétrico é um dos pilares do desenvolvimento tecnológico e industrial de uma nação. Dessa forma, é necessário gerir de uma maneira eficiente todos os recursos necessários para obtenção de energia elétrica. Os recursos hídricos se tornam essenciais já que o parque gerador brasileiro é predominantemente hidráulico. Neste contexto, o estudo da previsão de séries de vazões das usinas hidrelétricas tornou-se um campo de pesquisa altamente relevante para o planejamento da geração de energia no Brasil. Os modelos empregados pelo setor elétrico são os chamados modelos de Box & Jenkins, que exige um pré-tratamento dos dados de entrada por conta da sazonalidade encontrada nas vazões ao longo do ano. Este trabalho se utiliza de uma gama de modelos de previsão para comparação de desempenho no problema de previsão de séries de vazões médias mensais, em períodos distintos, da usina hidrelétrica de Furnas. Dentre os modelos lineares, é proposta a utilização de um dos modelos estatísticos, o Auto-regressivo e Médias Móveis (ARMA), tendo seus coeficientes calculados através de algoritmos bioinspirados: algoritmo genético e duas propostas de algoritmos imunológicos, uma baseada em pequenas alterações do CLONALG e a opt-aiNet. Em seguida, um filtro linear realimentado de resposta ao impulso infinita (IIR) tem seus coeficientes calculados pelos algoritmos de otimização acima citados. Na parte dos métodos nãolineares, fez-se a abordagem da aplicação de redes neurais artificiais do tipo perceptron de múltiplas camadas (MLP), com a utilização do algoritmo do gradiente conjugado escalonado modificado para o treinamento. Por fim, uma rede de estados de eco (ESN) é utilizada no problema, com dois algoritmos de treinamento: a proposta de Ozturk et al. E a de Consolaro. Os resultados experimentais mostram a aplicabilidade das ferramentas bioinspiradas e, em muitos casos, a relevância do laço de realimentação. No caso nãolinear, não foi possível obter resultados expressivos para a MLP, enquanto as ESN's mostraram alguns resultados promissores.Abstract: The Electric System is one of the pillars of technological and industrial development of a nation. Thus, it is necessary to manage in an efficient manner all necessary resources to obtain electrical energy. Water resources become essential since the Brazilian generator park is predominantly hydraulic. In this context, the study of prediction of the streamflow series of hydroelectric dams has become a field of research highly relevant to the planning of energy generation in Brazil. The models used by the electric sector are called models of Box & Jenkins, which requires pre-processing of input data due to the seasonality found in streamflow throughout the year. This work uses a range of forecasting models to compare performance in the problem of monthly averages streamflows series approached, in different periods, the hydroelectric power plant of Furnas. Among the linear models, it is proposed to use one of a statistical model, the autoregressive and moving average (ARMA), taking their coefficients calculated by bio-inspired algorithms: genetic algorithm and two proposed of immunological algorithms, one based on small changes in CLONALG and opt-aiNet. Then, a recurrent linear filter with the infinite impulse response (IIR) has its coefficients calculated by the optimization algorithms above. At the non-linear part, it is the approach of applying artificial neural networks of the type of multi-layer perceptron (MLP), using the algorithm of the modified scaled conjugate gradient for training. Finally, an echo states network is used in the problem, with two training algorithms: the proposal of Ozturk and of Consolaro. The experimental results show the applicability of bio-inspired tools and, in many cases, the importance of the loop of feedback. For the non-linear case, it was not possible to obtain significant results for the MLP, while the ESN's have shown some promising results.MestradoAutomaçãoMestre em Engenharia Elétric

Mlp-based Equalization And Pre-distortion Using An Artificial Immune Network

Due to its universal approximation capability, the multilayer perceptron (MLP) neural network has been applied to several function approximation and classification tasks. Despite its success in solving these problems, its training, when performed by a gradient-based method, is sometimes hindered by the existence of unsatisfactory solutions (local minima). In order to overcome this difficulty, this paper proposes a novel approach to the training of a MLP based on a simple artificial immune network model. The application domain for assessing the performance of the proposed technique is that of digital communications, in particular, the problems of channel equalization and pre-distortion. The obtained simulation results demonstrate that the proposal is capable of efficiently solving the problems tackled. © 2005 IEEE.177182Doering, A., Galicki, M., Witte, H., Structure optimization of neural networks with the a*-algorithm (1997) IEEE Transactions on Neural Networks, 8 (6), pp. 1434-1445Yao, X., Evolutionary artificial neural networks (1995) Encyclopedia of Computer Science and Technology, 33, pp. 137-170. , A. Kent and J. G. Williams, editors, Marcel Dekker Inc., New YorkGudise, V.G., Venayagamoorthy, G.K., Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks (2003) Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), pp. 110-117. , Indianapolis, Indiana, USADe Castro, L.N., Timmis, J.I., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-Verlag, LondonChen, S., Mulgrew, B., Grant, P.M., A clustering technique for digital communications channel equalization using radial basis function networks (1993) IEEE Trans, on Neural Networks, 4 (4), pp. 570-579Haykin, S., (1998) Neural Networks: A Comprehensive Foundation, , Prentice HallIbnkahla, M., Neural network predistortion technique for digital satellite communications (2000) Proceedings of ICASSP, 6, pp. 5-9. , JuneDe Castro, L.N., Timmis, J.I., Artificial immune systems as a novel soft computing paradigm (2003) Soft Computing Journal, 7 (8), pp. 526-544De Attux, R.R.F., Loiola, M.B., Suyama, R., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., Blind search for optimal wiener equalizers using an artificial immune network model (2003) EURASIP Journal of Applied Signal Processing, 2003 (8), pp. 740:747De Attux, R.R.F., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., A paradigm for blind IIR equalization using the constant modulus criterion and an artificial immune network (2003) Proceedings of the IEEE NNSP, , Toulouse, FranceDe Castro, L.N., Von Zuben, F.J., A hybrid paradigm for weight initialization in supervised feedforward neural network learning (1998) ICS-Workshop on Artificial Intelligence, pp. 30-37. , Tainan, Taiwan, DecemberBattiti, R., First- and second-order methods for learning: Between steepest descent and Newton's method (1992) Neural Computation, 4 (2), pp. 141-16