Artificial Intelligence based Pattern Recognition

Artificial intelligence based pattern recognition is one of the most important tools in process control to identify process problems. The objective of this study was to evaluate the relative performance of a feature-based Recognizer compared with the raw data-based recognizer. The study focused on recognition of seven commonly researched patterns plotted on the quality chart. The artificial intelligence based pattern recognizer trained using the three selected statistical features resulted in significantly better performance compared with the raw data-based recognizer

Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data

Motivation: Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted to simulate the gene–endophenotype–disease relationship in which endophenotypes were represented by hidden nodes

Learning and generalization in radial basis function networks

The aim of supervised learning is to approximate an unknown target function by adjusting the parameters of a learning model in response to possibly noisy examples generated by the target function. The performance of the learning model at this task can be quantified by examining its generalization ability. Initially the concept of generalization is reviewed, and various methods of measuring it, such as generalization error, prediction error, PAC learning and the evidence, are discussed and the relations between them examined. Some of these relations are dependent on the architecture of the learning model.Two architectures are prevalent in practical supervised learning: the multi -layer perceptron (MLP) and the radial basis function network (RBF). While the RBF has previously been examined from a worst -case perspective, this gives little insight into the performance and phenomena that can be expected in the typical case. This thesis focusses on the properties of learning and generalization that can be expected on average in the RBF.There are two methods in use for training the RBF. The basis functions can be fixed in advance, utilising an unsupervised learning algorithm, or can adapt during the training process. For the case in which the basis functions are fixed, the typical generalization error given a data set of particular size is calculated by employing the Bayesian framework. The effects of noisy data and regularization are examined, the optimal settings of the parameters that control the learning process are calculated, and the consequences of a mismatch between the learning model and the data -generating mechanism are demonstrated.The second case, in which the basis functions are adapted, is studied utilising the on -line learning paradigm. The average evolution of generalization error is calculated in a manner which allows the phenomena of the learning process, such as the specialization of the basis functions, to be eludicated. The three most important stages of training: the symmetric phase, the symmetry- breaking phase and the convergence phase, are analyzed in detail; the convergence phase analysis allows the derivation of maximal and optimal learning rates. Noise on both the inputs and outputs of the data -generating mechanism is introduced, and the consequences examined. Regularization via weight decay is also studied, as are the effects of the learning model being poorly matched to the data generator

Quantifica??o de biomassa em floresta estacional semidecidual por meio de redes neurais artificiais

RESUMO CUNHA, E. G. S. Quantifica??o de biomassa em floresta estacional semidecidual por meio de redes neurais artificiais. 2015. 77 p. (Disserta??o ? Ci?ncia Florestal) ? Universidade Florestal dos Vales do Jequitinhonha e Mucuri, Diamantina, 2015. A predi??o de biomassa em florestas naturais ? complexa devido ? varia??o de esp?cies, de est?gio sucessional, caracter?sticas ed?ficas e clim?ticas das ?reas, dentre outras, e isso gera uma grande demanda de informa??es para que se tenha estimativas de biomassa confi?veis. O objetivo deste estudo foi de quantificar a biomassa arb?rea a?rea de um fragmento de floresta Estacional Semidecidual em Minas Gerais - MG por meio de redes neurais artificiais (RNA). Assim como, avaliar a influ?ncia das vari?veis categ?ricas fitofisionomia (FT), infesta??o de cip? (CP), qualidade de copa (QC) e coeficiente de De Liocourt (q) na estimativa de biomassa. Foi empregada a t?cnica de valida??o cruzada (cross-validation) para defini??o da topologia e valida??o das redes, em que a estimativa de erro global ? calculada como a m?dia das k estimativas de erro de cada itera??o (admitiu-se k=10).Variou-se o n?mero de neur?nios na camada escondida e avaliou-se a m?dia e o desvio padr?o do erro m?dio quadr?tico (EMQ) dos resultados da valida??o cruzada para definir o n?mero de neur?nios na camada escondida, que melhor se adequou ao problema. Para definir a RNA mais adequada para cada situa??o, uma nova valida??o cruzada foi realizada e avalia??o se deu pelos ajustes das RNA (EMQ, ? ,Bias e Vari?ncia) e an?lise gr?fica dos res?duos. A biomassa observada m?dia foi de 110,81 t.ha-1 e a biomassa m?dia estocada por hectare foi estimada em 114,41 t.ha-1 pela RNA 3 e 116,34 t.ha-1 pela RNA 7. As RNA 3(vari?veis de entrada: DAP, d, Hf, Vt, CP) e 7(vari?veis de entrada: DAP, d, Ht, Vt, CP, QC) se ajustaram melhor, obtendo menores res?duos. No entanto, a RNA 7 que cont?m CP e QC associadas como vari?vel de entrada da rede, teve bom desempenho devido ? contribui??o da vari?vel CP, visto que a RNA 4 que cont?m apenas QC n?o foi precisa na maioria das parcelas, esse fato pode estar relacionado a maior subjetividade na avalia??o da qualidade de copa. A RNA 3 foi mais adequada visando simplicidade na coleta de campo, acarretando menor tempo e custo.Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Ci?ncia Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2015.ABSTRACT CUNHA, E. G. S. Biomass quantification in semideciduous forest through artificial neural networks. 2015. 77 p. (Disserta??o ? Ci?ncia Florestal) ? Universidade Florestal dos Vales do Jequitinhonha e Mucuri, Diamantina, 2015. The prediction of biomass in natural forests is complex due to the variation of species in successional stages, characteristics of soil and climate of areas, among other things, and this generates a large demand for information in order to produce reliable biomass estimates. This study aimed to quantify the aerial tree biomass from a fragment of semideciduous forest in Minas Gerais - MG through artificial neural networks (ANN). Moreover, this study evaluated the influence of categorical variables including phytophysiognomy, vine infestation, canopy quality and the De Liocourt quotient in estimation of biomass. All the analysis was done using R software, using the cross-validation technique to define the topology and validation of networks, wherein the global error estimate is calculated as the average of k error estimates of each iteration (assuming k = 10). The number of neurons in the hidden layer varied, and the mean and standard deviation of the mean square error (MSE) were evaluated by the results of cross-validation in order to determine the number of neurons in the hidden layer, which best adapted to the problem. To determine the most appropriate ANN in each situation, a new cross-validation was conducted and the evaluation was completed using the results of the ANN (MSE, Correlation coefficient, Bias and Variance) and graphical residue analysis. The average observed biomass was 110.81 t.ha-1 and the average stored biomass per hectare was estimated to be 114.41 t.ha-1 using ANN 3 and 116.34 t.ha-1 using 7 ANN. This study found that the ANN 3 (input: DAP, d, Hf, Vt, CP) and 7(input: DAP, d, Ht, Vt, CP, QC) had greater precision than the other ANNs, obtaining smaller residue. However, due to the association of vine infestation and crown quality in ANN 7 as input variable network, the good performance possibly because of the variable contribution of the vine infestation, whereas ANN 4 which contains only canopy quality was not more accurate in the majority of plots, this may be related to greater subjectivity in assessing the canopy quality, and in this way. ANN 3 was more appropriate in terms of simplifying field sampling, leading to reduced time and cost

Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective?

A statistical theory for overtraining is proposed. The analysis treats realizable stochastic neural networks, trained with KullbackLeibler loss in the asymptotic case. It is shown that the asymptotic gain in the generalization error is small if we perform early stopping, even if we have access to the optimal stopping time. Considering cross-validation stopping we answer the question: In what ratio the examples should be divided into training and testing sets in order to obtain the optimum performance. In the non-asymptotic region cross-validated early stopping always decreases the generalization error. Our large scale simulations done on a CM5 are in nice agreement with our analytical findings

Previsor neural de carga elétrica baseado em análise de multiresolução via wavelets e técnicas de reconstrução do espaço-fase

The importance of short-term load forecasting has been increasing lately. With deregulation and competition, energy price forecasting has become a big business. Bus-load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and non-stationarity of loads are becoming worse due to the dynamics of energy prices. Besides, the number of nodal loads to be predicted does not allow frequent interactions with load forecasting experts. More autonomous load predictors are needed in the new competitive scenario. This thesis deals with two main research lines. In the first one, two different strategies for successfully embedding the Discrete Wavelet Transform into Artificial Neural Networksbased short-term load forecasting is presented. The first strategy is new. It consists of creating a model for load forecasting whose inputs are based on information from the original load sequence and from wavelet domain subseries, as well. The second alternative predicts the load’s future behavior by independently forecasting each subseries in the wavelet domain. The other research line evaluates the feasibility of a nonlinear criterion based on the method of delay coordinates for determining the best set of input variables for a neural forecaster. This criterion is fully compared to another linear criterion based on the autocorrelation function. The main goal of this work is to develop more robust load forecasting algorithms. Hourly load and temperature data for a North-American electric utility are used to test the proposed methodologies.A importância da previsão de carga a curto prazo tem crescido ultimamente. Com a desregulamentação e a competição advinda desse processo, a previsão do preço de energia se transformou em uma atividade bastante lucrativa. A previsão das cargas das barras é essencial para alimentar métodos analíticos utilizados para determinar os preços de energia. A variabilidade e a não estacionariedade das cargas estão ficando cada vez piores devido à dinâmica dos preços de energia. Além disso, o número de cargas nodais a serem previstas não permite interações freqüentes com os especialistas em previsão de carga. Portanto, previsores de carga mais autônomos são necessários nesse novo cenário competitivo. Esta tese apresenta duas linhas de pesquisa diferentes. Na primeira delas, duas estratégias para a utilização da transformada wavelet na previsão de carga via redes neurais são apresentadas. A primeira estratégia é nova. Ela consiste na criação de um modelo de previsão de carga cujas entradas são baseadas na informação da série de carga original e na informação fornecida pelas subséries no domínio wavelet. Já na segunda estratégia, o comportamento futuro da carga é conseguido através da combinação de previsões independentes de cada subsérie no domínio wavelet. A segunda linha de pesquisa investiga a aplicabilidade de uma metodologia não linear baseada no método de coordenadas em atraso para a seleção das variáveis de entrada mais significativas para previsores neurais. Esse critério é comparado com um outro critério linear baseado na função de autocorrelação. Com a utilização das metodologias supraditas, objetiva-se o desenvolvimento de previsores de carga mais robustos. Para testá-las, dados horários reais de carga e temperatura de uma concessionária de energia elétrica norte-americana são utilizados