Snjezana Soltic

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This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the(More)
The paper investigates the use of the spiking neural networks for taste recognition in a simple artificial gustatory model. We present an approach based on simple integrate-and-fire neurons with rank order coded inputs where the network is built by an evolving learning algorithm. Further, we investigate how the information encoding in a population of(More)
The authors build on previous experience in the optimization of white-light sources based on combinations of narrow-band spectra. They extend those concepts by using delta-function spectra to study the prospects of future optimal laser-based sources. The optimization process is based on a trade-off between the color rendering properties and the luminous(More)
The paper introduces a statistical model and a DENFIS-based model for estimating the potential establishment of a pest insect. They have a common probability evaluation module, but very different clustering and regression modules. The statistical model uses a typical K-means algorithm for data clustering , and a multivariate linear regression to build the(More)
This paper introduces an evolving computational and a statistical model for quantitatively estimating the establishment potential of a pest insect and compares their performances. The models were used to predict the establishment potential of Planocuccus citri (Risso), the citrus mealybug. They have the common clustering and probability evaluation modules,(More)
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