In this paper, a new adaptive neural network classifier (ANNC) of EEGP300 signals from mental activities is proposed. To overcome an overtraining of the classifier caused by noisy and non-stationary data, the EEG signals are filtered and their autoregressive (AR) properties are extracted using an AR model before being passed to the ANNC. For evaluation purposes, the same data in Hoffmann et al....

The applications of time series modeling and statistical similarity methods to structural health monitoring (SHM) provide promising and capable approaches to structural damage detection. The main aim of this article is to propose an efficient univariate similarity method named as Kullback similarity (KS) for identifying the location of damage and estimating the level of damage severity. An impr...

In our SUGI 2006 presentation, we suggested using low-order autoregressive models, AR(1) and AR(2), in biosurveillance and outbreak detection (PROC ARIMA, SAS/ETS). Our suggestion was based on empirical data. In the NESUG 2007 paper, we proposed strong theoretical grounds for this. Here we provide further development of our approach. Based on a classic susceptibleinfectious-recovered (SIR) mode...

Balanced Model Truncation (BMT) is a powerful technique that can be used to greatly reduce the order of certain digital filters with little distortion to their frequency and phase responses. Since digital filters are commonly used in computer music applications, BMT may be a tool of considerable practical use. To demonstrate this potential, BMT is applied to an autoregressive (AR) drum sound mo...

Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models combining the strengths of both models. Our contribution is to train such hybrid models using an auxiliary...

This article investigates a theoretical basis for estimating autoregressive (AR) processes for linear-recurrent signals in convolutive mixtures. Whitening of such signals is sometimes a problem in multichannel blind equalization which is intended to extract the original signals even if the signals are of a convolutive mixture type. This whitening is due to inverse-filtering which deconvolves th...

introduction: advanced oxidation processes (aops) suggest a highly reactive, nonspecific oxidant namely hydroxyl radical (oh•), that oxidize a wide range of pollutants fast and non-selective in wastewater and water. materials and methods: in this work, the nitrogen-doped titanium dioxide nanoparticles were primed by sol-gel method, characterized by x-ray diffraction and scanning electron micros...

We consider the problem of classifying multiple simultaneous autoregressive (AR) signals based upon the observation of their sum using a multilayer perceptron network (MLP). We propose a method that allows the training of the classiier to be performed on separate AR processes , and uses the Bayesian interpretation of the outputs of a MLP to obtain the maximum a posteriori probability decomposit...

Most models for the time series of stock prices have centered on autoregressive (AR) processes. Traditionaly, fundamantal Box-Jenkins analysis [2] have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. A fuzzy regression model is then introduced. Following this description, an artificial fu...

Most models for the time series of stock prices have centered on autoregressive (AR) processes. Traditionally, fundamental Box-Jenkins analysis have been the mainstream methodology used to develop time series models. We briefly describe developing a classical AR model for stock price forecasting. Then a fuzzy regression model is introduced. Following this description, an artificial fuzzy neural...