Neuro-fuzzy system for diagnosis of engines, based on oil samples analysis

The present paper describes a neuro-fuzzy. hybrid system applied to the diagnosis of automobile engines, based on the analysis of oil samples. A relevance analysls was done to select the most significant variables among the avallable ones, in order to classify the samples. Such relevance analysls is described in detalls along the paper. Four dlfferent systems were implemented one pure neural system, and three dlfferent neuro-fuzzy systems. A detailed descriptlon of the neural and fuzzy systems is also presented, as well as the performance obtained by each one of them

Avaliação de redes neurais aplicadas à previsão de índices de mercados de ações

Artificial neural networks have been utilized in modeling solutions for time series forecasting problems arisen in different financial area segments such as financial statements analysis, macro-economic indicators, currency market, stock quotations and market indexes. When dealing with such problems, the forecasting model quality is usually appraised in terms of the difference between the actual value and the network forecasted value. But financial area applications also require that related financial goals such as profitability and low risk exposure be considered. The commonly used mean square error generally does not grant those needs are met. It seems then to be necessary to establish additional criteria considering specific financial goals. The current paper shows the results obtained in experiments carried on to compare different neural network architectures to forecast the São Paulo Stock Exchange Ibovespa index using different training criteria and performance evaluation strategies based on business and financial goalsAs redes neurais artificiais vêm sendo utilizadas na modelagem da solução de problemas de previsão de séries temporais em diferentes segmentos da área financeira, como por exemplo, análise de balanços, indicadores macroeconômicos, mercado de câmbio, cotação de ações e índices de mercados. Nesses problemas, é usual mensurar a qualidade do modelo de previsão através do uso de alguma medida de erro entre o valor real e o valor previsto pela rede. Contudo, aplicações na área financeira demandam o atendimento a objetivos financeiros subjacentes, tais como nível de lucratividade ou exposição ao risco. A obtenção de medidas de erro com magnitude pouco significativa não é garantia de atendimento a esses objetivos: há necessidade do estabelecimento de critérios adicionais, de forma a possibilitar a aferição da qualidade dos resultados obtidos à luz de objetivos financeiros específicos. Este artigo apresenta resultados de alguns experimentos realizados com vistas a comparar diferentes arquiteturas de redes neurais para a previsão do índice Ibovespa, da Bolsa de Valores de São Paulo, envolvendo diferentes critérios de treinamento e estratégias de avaliação do desempenho com base em objetivos financeiros e de negócio

Automatic speech recognition: a study and performance evaluation on neural networks and hidden markov models

The main goal in this research is to find out possible ways to built hybrid systems, based on neural network (NN) and hidden M;arkov (HMM) models, for the task of automatic speech recognition. The investigation that has been conducted covers different types of neural network and hidden Markov models, and the combination of them into some hybrid models. The neural networks used were basically MLP and Radial Basis models. The hidden Markov models were basically different combinations of states and mixtures of the Continuous Density type of the Bakis model. A reduced set with ten words spoken in the Portuguese idiom, from Brazil, was carefully chosen to provide some pronounce and phonetic confusion. The results already obtained showed very positive, pointing toward to a high potentiality of such hybrid models

Proposing a customized exokernel library to data mining

The implementation of customized system libraries in an exokernel environment is considered as a promising approach in optimizing data mining processes. Customized libraries in exokernel environments have been successfully used in optimizing other applications, and is potentially suitable to demanding applications such as data mining. A prototype, to test our hypothesis, is under construction. This work introduces data mining, the exokernel environment and describes our prototype's building strategy

Automatic speech recognition: a comparative evaluation between neural networks and hidden markov models

In this work we do a comparative evaluation between Artificial Neural Networks (RNA's) and Continuous Hidden Markov Models (CDHMM), in the framework of the recognition of isolated words, under the constrain of using a small number of features extracted from each voice signal. In order to accomplish such comparison we used two models of neural networks: the Multilayer Perceptron (MLP) and a variant of the Radial Basis (RBF), and some HMM models. We evaluated the performance of all models using two different test set and observed that the neural models presented the best results in both cases. Seeking to improve the HMM performance we developed a hybrid system, HMM/MLP, that improved the results previously obtained with all HMMs, and even those obtained with the neural networks for the all previous HMM, and even the neural nets for the hardest test set case

Uma proposta de avaliação da arquitetura exokernel para mineração de dados

This paper has been originated from the need to optimize data mining applications and to reduce their Computacional resource demand. Recent researches are tipically concentrated on the search for faster algorithms. Another approach, presented in this paper, intends to provide the operating systems with facilities to better support those applications workload. In order to validate this approach, we propose the implementation of customized libraries in the Exokernel architecture