Dimitris Varsamis

On the Newton multivariate polynomial interpolation with applications

The main purpose of this work is to provide an optimized version of the Newton divided-difference algorithm presented by [11] for the polynomial interpolation problem. This optimized algorithm is used for the computation of the determinant of a two-variable polynomial matrix.

Forecasting of telecommunications time-series via an Orthogonal Least Squares-based fuzzy model

An application of fuzzy modeling to the problem of telecommunications data prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on the Orthogonal Least Squares (OLS) technique. Particularly, the OLS is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, a second orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. Input selection is automatically performed, given a large input candidate set. Real world telecommunications data are used in order to highlight the characteristics of the proposed forecaster and to provide a comparative analysis with well-established forecasting models.

On the Newton bivariate polynomial interpolation with applications

The main purpose of this work is to provide recursive algorithms for the computation of the Newton interpolation polynomial of a given two-variable function. The special case where the interpolation polynomial has known upper bounds on the degree of each indeterminate is studied and applied to the computation of the inverse of a two-variable polynomial matrix.

Inset Mapper: A Software Tool In Island Cartography

Island cartography deals with special cartographic problems confronted in the portrayal of island regions and demands the use of specially developed software tools. One of the most commonly faced problems is the need of inset map creation for very small islands, and sometimes isolated ones, that must be displayed in the main map. This paper presents the methodology followed for the development of the Inset Mapper (IM) Software toolbox, describes the toolbox, and showcases its ability to create inset maps in Island regions. The IM software tool provides a useful cartographic tool for assisting the selection of the most appropriate position and scale of the inset map in an Island region.

On a special case of the two-variable Newton interpolation polynomial

The paper, proposes two new algorithms for the construction of a two-variable Newton-interpolation polynomial, for the special case where we have a rectangular basis with equidistant points. The complexity of the proposed algorithms is better than the ones given in [1] since it is based only on additions. One of the two algorithms is based on matrix multiplications and thus it is easily implemented in programming languages which supports such kind of operations.

A Recurrent Fuzzy Filter for Adaptive Noise Cancellation

This paper presents a recurrent fuzzy-neural filter for adaptive noise cancellation. The cancellation task is transformed to a system-identification problem, which is tackled by use of the dynamic neuron-based fuzzy neural network. Extensive simulation results are given and performance comparison with a series of other dynamic fuzzy and neural models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals

Telecommunications call volume forecasting with a block-diagonal recurrent fuzzy neural network

An application of computational intelligence to the problem of telecommunications call volume forecasting is proposed in this work. In particular, the forecasting system is a recurrent fuzzy-neural model. The premise and defuzzification parts of the model’s fuzzy rules are static, while the consequent parts of the fuzzy rules are small block-diagonal recurrent neural networks with internal feedback, thus enabling the overall system to discover the temporal dependencies of the telecommunications time-series and perform forecasting without requiring prior knowledge of the exact order of the time-series. The forecasting performance is evaluated by using real-world telecommunications data. An extensive comparative analysis with a series of existing forecasters is conducted, including both traditional models as well as computational intelligence’s approaches. The simulation results confirm the modelling potential of the proposed scheme, since the latter outperforms its competing rivals in terms of three appropriate metrics, in all kinds of calls.

A telecommunications call volume forecasting system based on a recurrent fuzzy neural network

The problem of telecommunications call volume forecasting is addressed to in this work. In particular, a foreacasting system is proposed, that is based on a dynamic fuzzy-neural model, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks with internal feedback. The forecasting characteristics are highlighted and the prediction performance is evaluated by use of real-world telecommunications data. An extensive comparative analysis with a series of existing forecasters is conducting, including both traditional models as well as fuzzy and neurofuzzy approaches.

A TSK-based fuzzy system for telecommunications time-series forecasting

A two-stage model-building process for generating a Takagi-Sugeno-Kang fuzzy forecasting system is proposed in this paper. Particularly, the Subtractive Clustering (SC) method is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, an Orthogonal Least Squares (OLS) estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. A comparative analysis with well-established forecasting models is conducted on real world tele-communications data, in order to investigate the forecasting capabilities of the proposed scheme.

A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation

This paper presents a recurrent fuzzy-neural filter for adaptive noise cancelation. The cancelation task is transformed to a system-identification problem, which is tackled by use of the dynamic neuron-based fuzzy neural network (DN-FNN). The fuzzy model is based on Takagi–Sugeno–Kang fuzzy rules, whose consequent parts consist of linear combinations of dynamic neurons. The orthogonal least squares method is employed to select the number of rules, along with the number and kind of dynamic neurons that participate in each rule. Extensive simulation results are given and performance comparison with a series of other dynamic fuzzy and neural models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.