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Classical approaches to the fault detection and isolation usually require extensive plant-modeling and statistical analysis of the measured signals and their residuals versus the developed model. In this paper, alternative simple model-free approach is proposed. Real-time data are preprocessed and self-organizing map is trained and used for the reliable isolation of the most frequent mill fault -...
Energy consumption predictions are essential and are required in the studies of capacity expansion, energy supply strategy, capital investment, revenue analysis and market research management. In the recent years artificial neural networks (ANN) have attracted much attention and many interesting ANN applications have been reported in power system areas, due to their computational speed, their ability...
In this paper, a method for classifying room impulse responses using multifractals and Kohonen's neural networks is investigated. Impulse response is basic source of information in room acoustics; therefore its analysis is the most important issue regarding sound impression in the room. The method proposed in this paper consists of three steps. The first stage of signal classification process is computation...
Influence of the interaction time-delay on the noise induced system size resonance in a system of all-to-all electrically coupled FitzHugh-Nagumo excitable neurons is studied. It is observed that small time-lags decrease and that large time-lags increase the coherence of spiking. Bifurcations of the system's stationary state are used to explain the observed non-monotonic dependence of coherence on...
Two modern concepts implemented for forecasting based on reduced time series are contrasted. Results obtained by use of artificial neural nets (ANNs), already discussed at this conference, are compared with the ones obtained by implementation of the so called Grey theory or Grey Model (GM). Particularly, feed-forward accommodated for prediction (FFAP) and time controlled recurrent (TCR) ANNs are used...
This paper proposes novel approach in coding single phonemes based on mel-frequency cepstral coefficients (MFCC) in order to simplify the neural network used to recognize those phonemes. The efficiency and effectiveness of proposed algorithm are demonstrated for both male and female speakers.
Alternative and more efficient approaches for microwave applicators modeling, in respect to conventional numerical techniques, are presented in the paper. These approaches are based on artificial neural networks incorporating previously acquired partially knowledge about problem domain. Neural models, created using the suggested approaches, provide the similarly accuracy as numerical methods but performing...
We investigate performance of neural networks for classification of laser-induced breakdown spectroscopic data of four proteins: Bovine Serum Albumin, Osteopontin, Leptin and Insulin-like Growth Factor II. We utilize principal component analysis algorithm for feature extraction and multilayer perceptrons algorithms with one and two hidden layers. We employ leave-one-out procedure for classifier evaluation...
The aim of this paper is to show that the data stored in companies data warehouses can be used in order to improve business. By application of data mining method, neural clustering, we investigate age structure of employees and its influence on business companies. This would enable improvement in employment policy for small and medium-sized companies. The criteria while employing new people in retail...
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