Blind Image Restoration by Combining Wavelet Transform and RBF Neural Network

In this paper, we present a novel technique for restoring a blurred noisy image without any prior knowledge of the blurring function and the statistics of noise. The technique combines wavelet transform with radial basis function (RBF) neural network to restore the given image which is degraded by Gaussian blur and additive noise. In the proposed technique, the wavelet transform is adopted to decompose the degraded image into high frequency parts and low frequency part. Then the RBF neural network based technique is used to restore the underlying image from the given image. The inverse principal element method (IPEM) is applied to speed up the computation. Experimental results show that the proposed technique inherited the advantages of wavelet transform and IPEM, and the algorithm is efficient in computation and robust to the noise

Application of adaptive wavelet networks for vibration control of base isolated structures

Accepted version of an article from the journal: International Journal of Wavelets, Multiresolution & Information Processing. Official version article published as International Journal of Wavelets, Multiresolution & Information Processing, 2010 8(5), 773-791. doi: 10.1142/s0219691310003778 © World Scientific Publishing Company http:// http://www.worldscinet.com/ijwmip/This paper presents an application of wavelet networks (WNs) in identification and control design for a class of structures equipped with a type of semiactive actuators, which are called magnetorheological (MR) dampers. The nonlinear model is identified based on a WN framework. Based on the technique of feedback linearization, supervisory control and H∞ control, an adaptive control strategy is developed to compensate for the nonlinearity in the structure so as to enhance the response of the system to earthquake type inputs. Furthermore, the parameter adaptive laws of the WN are developed. In particular, it is shown that the proposed control strategy offers a reasonably effective approach to semiactive control of structures. The applicability of the proposed method is illustrated on a building structure by computer simulation

Optimisasi Model Fuzzy Terbobot untuk Klasifikasi Data Polikotomus dan Penerapannya di Bidang Kesehatan

Penelitian ini bertujuan untuk mengembangkan metode baru dalam pemodelan fuzzy untuk klasifikasi data polikotomus dengan kombinasi metode aturan fuzzy terbobot (weighted fuzzy rule) dan dekomposisi nilai singular serta mengaplikasikannya untuk mendiagnosis penyakit kanker serviks dan kanker payudara. Target khusus dalam penelitian ini adalah mendapatkan metode baru dalam pemodelan fuzzy terbobot yang optimal untuk klasifikasi data polikotomus, menghasilkan pemrograman graphical user interface (GUI) untuk model fuzzy terbobot yang optimal untuk data polikotomus, dan menerapkannya untuk klasifikasi di bidang kesehatan yaitu untuk diagnosis kanker serviks dan kanker payudara. Pada penelitian tahun pertama, telah dibangun suatu prosedur baru dalam pembentukan model fuzzy Mamdani yang optimal untuk klasifikasi data polikotomus dengan metode aturan fuzzy terbobot. Kemudian dibangun suatu prosedur baru dalam pembentukan model fuzzy Takagi-Sugeno-Kang (TSK) order satu dengan kombinasi metode aturan fuzzy terbobot dan dekomposisi nilai singular. Berdasarkan prosedur tersebut, dikembangkan pemrograman graphical user interface (GUI) dengan MATLAB untuk klasifikasi data polikotomus. Selanjutnya pada tahun kedua, hasil pada tahun pertama akan diterapkan untuk menyelesaikan permasalahan klasifikasi di bidang kesehatan khususnya untuk diagnosis kanker serviks dan kanker payudara

Optimisasi Model Fuzzy Terbobot untuk Klasifikasi Data Polikotomus dan Penerapannya di Bidang Kesehatan

Penelitian ini bertujuan untuk mengembangkan metode baru dalam pemodelan fuzzy untuk klasifikasi data polikotomus dengan kombinasi metode aturan fuzzy terbobot (weighted fuzzy rule) dan dekomposisi nilai singular serta mengaplikasikannya untuk mendiagnosis penyakit kanker serviks dan kanker payudara. Target khusus dalam penelitian ini adalah mendapatkan metode baru dalam pemodelan fuzzy terbobot yang optimal untuk klasifikasi data polikotomus, menghasilkan pemrograman graphical user interface (GUI) untuk model fuzzy terbobot yang optimal untuk data polikotomus, dan menerapkannya untuk klasifikasi di bidang kesehatan yaitu untuk diagnosis kanker serviks dan kanker payudara. Pada penelitian tahun pertama, telah dibangun suatu prosedur baru dalam pembentukan model fuzzy Mamdani yang optimal untuk klasifikasi data polikotomus dengan metode aturan fuzzy terbobot. Kemudian dibangun suatu prosedur baru dalam pembentukan model fuzzy Takagi-Sugeno-Kang (TSK) order satu dengan kombinasi metode aturan fuzzy terbobot dan dekomposisi nilai singular. Berdasarkan prosedur tersebut, dikembangkan pemrograman graphical user interface (GUI) dengan MATLAB untuk klasifikasi data polikotomus. Selanjutnya pada tahun kedua, hasil pada tahun pertama akan diterapkan untuk menyelesaikan permasalahan klasifikasi di bidang kesehatan khususnya untuk diagnosis kanker serviks dan kanker payudara

Previsão de demanda de energia elétrica de curto prazo utilizando abordagens de comitês de Wavenets

Orientador : Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 12/04/2017Inclui referências : f. 87-98Área de concentração: Sistemas eletrônicosResumo: A energia elétrica faz parte de um mercado que envolve agentes de geração, transmissão, distribuição e consumo que desejam maximizar seus lucros e minimizar suas despesas. Para isso precisam de um planejamento que tenha como base uma previsão de demanda precisa, já que um cenário pessimista pode levar ao despacho de mais geradores do que o necessário, reserva excessiva de matéria prima e aumento do custo de operação, e por outro lado um cenário otimista pode colocar o sistema elétrico em risco ou exigir a compra de energia no mercado livre a um preço alto. Por isso, a previsão de demanda tem sido empregada em áreas como o agendamento ótimo de geradores, planejamento da manutenção, planejamento da reserva hídrica, compreensão do padrão de consumo, planejamento da expansão e previsão de preços e ajuste de tarifas. Contudo, uma série de demanda é uma série temporal que possui não linearidades e componentes periódicos aleatórios como o clima, perfil dos usuários, eventos públicos, economia, medições erradas, e, consequentemente, um modelo de previsão linear pode não ser apropriado. Este trabalho utiliza diferentes abordagens para formar comitês de wavenets para a previsão de séries temporais de demanda de energia elétrica de curto prazo, os desempenhos são comparados com uma rede neural artificial perceptron multicamadas com função de ativação sigmoide na camada oculta, uma wavenet simples, com a média da última semana e com o modelo inocente. As séries de demanda adotadas, isto é, duas séries de demanda anuais reais com medições horárias, passam por um estágio de pré-processamento para remoção da tendência e normalização, e também para transformação dos valores da série em conjuntos de entrada e saída para o treinamento supervisionado. Emprega-se a estratégia de previsão um passo à frente e a avaliação das previsões é realizada pelo coeficiente de correlação múltipla ???? e também pela análise de correlação entre os resíduos. Para criação dos comitês utiliza-se a reamostragem com reposição, a validação cruzada e a dizimação de entradas, seleção construtiva, combinação pela média simples, moda, mediana e generalização empilhada. Os resultados dos testes de não linearidade demonstram que as duas séries consideradas são não lineares, e também constata-se a diminuição da assimetria dos dados após sua transformação. Do processo de seleção de variáveis obtém-se os atrasos máximos para cada série, valores passados que são utilizados como entradas, e percebe-se que são diferentes para cada série. O atraso máximo a ser utilizado como entrada tem influência na quantidade de amostras do conjunto de dados de entrada e saída. Uma característica dos resultados que se reflete em ambas as séries é o aumento do erro à medida que o horizonte de previsão aumenta. Os comitês de wavenets superam os demais modelos comparados, e, além do desempenho ser diferente para cada problema, o melhor método de aprendizado de comitê a ser utilizado também varia, bem como o horizonte de previsão máximo no qual os valores previstos se ajustam aos valores reais das séries. A qualidade das previsões é avaliada com testes de correlação dos resíduos. Palavras-chave: Wavenet. Previsão de demanda de energia elétrica. Comitês. Redes neurais artificiais.Abstract: Electricity is part of a market which involves generation, transmission, distribution and consumption agents that aim their profit maximization and expenses minimization. To achieve that, they need a planning based on an accurate load forecast, since a pessimistic scenario may lead to more generators dispatch than needed, excessive reservoir and high operating costs, and, on the other hand, an optimistic scenario may place the electrical system at risk or requiring demand electricity purchasing on the free market for a very high price. Hence, load forecasting has been employed in areas such as optimal dispatch, maintenance planning, hydric reservoir planning, consumption pattern understanding, expansion planning, price forecasting and tax adjustments. However, a load series is a time series with nonlinearities and random periodic components as the weather, users profile, public events, economy and bad measures, therefore a pure linear model may not be appropriated. This work uses different approaches to create wavenet ensembles for short term load forecasting, the performances are compared with a multilayer perceptron with sigmoid activation function in the hidden layer, with a single wavenet, with the last week mean and also with the naive model. The load series adopted, that is, two annual hourly load series with actual measurements, are passed through a data pre-processing stage for trend removal and normalization, and also for conversion from the time series to a inputs and output set for supervised training. It is applied the one step ahead forecast strategy and the forecasting evaluation is accomplished by the multiple correlation coefficient, ????, and also by the residuals correlation analysis. For the ensemble creation are used the bootstrapping, cross-validation like, inputs decimation, constructive selection, simple average, median, mode and stacked generalization methods. The nonlinearity tests results demonstrate that both time series are nonlinear, and the asymmetry reduction after data transformation is verified. From the features selection process the maximum lags for each series are identified, lagged values to be used as inputs and it is noticed that they are different for each series. The maximum lag also influences the amount of samples in the dataset of inputs and outputs. A common characteristic of both series is that the error increase along with the prediction horizon. Results point out that the wavenets ensembles overcome the other compared models after tests with two actual annual hourly load series. Moreover, beyond the performance to be different for each problem, the best ensemble learning method also varies, as well as the maximum forecasting horizon for which the forecasted values fit the series actual values. The quality of the forecasts is analyzed through residuals correlation tests. Key-words: Wavenet. Load forecasting. Ensembles. Artificial neural network

A Review of Wavelet Networks, Wavenets, Fuzzy Wavenets and Their Applications

The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelet networks have been used in classification and identification problems with some success. The strength of wavelet networks lies in their capabilities of catching essential features in "frequency-rich" signals. In wavelet networks, both the position and the dilation of the wavelets are optimized besides the weights. Wavenet is another term to describe wavelet networks. Originally, wavenets did refer to neural networks using dyadic wavelets. In wavenets, the position and dilation of the wavelets are fixed and the weights are optimized by the network. We propose to adopt this terminology. The theory of wavenets has been generalized by the author to biorthogonal wavelets. This extension to biorthogonal wavelets has lead to the development of fuzzy wavenets. A serious difficulty with most neurofuzzy methods is that they do often furnish rules without a transparent interpretation. A solution to this problem is furnished by multiresolution techniques. The most appropriate membership functions are chosen from a dictionary of membership functions forming a multiresolution. The dictionary contains a number of membership functions that have the property to be symmetric, everywhere positive and with a single maxima. This family includes among others splines and some radial functions. The main advantage of using a dictionary of membership functions is that each term, such as "small", "large" is well defined beforehand and is not modified during learning. The multiresolution properties of the membership functions in the dictionary function permit to fuse or split membership functions ..