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In this paper we present mathematical model of well known Delta-Notch signaling system in form of nonlinear, singular, impulsive and singularly impulsive dynamical systems. This example is interesting since it posses phenomena of lateral inhibition recognized to be underlying element in mechanism structures in many living organisms. It is characterized by beautiful patterns as a result of singling...
In this paper, an efficient heart beat classification algorithm suitable for implementation on mobile devices is presented. A simplified ECG model is used for feature extraction in the time domain. The QRS complex is modeled using straight lines, while P and T waves are modeled using parabolas. The model parameters are estimated by minimizing the root mean square (RMS) of the model error. Heart beats...
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...
This paper investigates the effects of using limited precision for efficient implementations of the RBF-M neural network. This architecture employs only simple arithmetic operators and is characterized by simple LMS training in an expanded feature space generated by RBF functions centered around support vectors selected via a simple algorithm. The classification performances of our low complexity,...
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...
In this paper, the author's previous work is extended and a new neural network is utilized to solve the stability problem of multidimensional systems. In the original authors work the problem is transformed into an optimization problem. Using the DeCarlo-Strintzis Theorem one has to check if |B(Z1,..., 1, Zm)| ≠ 0 for |Z1| = ... = |Zm| = 1 or equivalently if the min |B(Z1, ..., 1, Zm)| is 0 or not,...
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