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Τεκμήριο A computational intelligence-based forecasting system for telecommunications time series(2012-02) Mastorocostas, Paris; Hilas, ConstantinosIn this work a computational intelligence-based approach is proposed for forecasting outgoing telephone calls in a University Campus. A modified Takagi–Sugeno–Kang fuzzy neural system is presented, where the consequent parts of the fuzzy rules are neural networks with an internal recurrence, thus introducing the dynamics to the overall system. The proposed model, entitled Locally Recurrent Neurofuzzy Forecasting System (LR-NFFS), is compared to well-established forecasting models, where its particular characteristics are highlighted.Τεκμήριο Fuzzy Cluster Analysis and Simplification(Τ.Ε.Ι. Κεντρικής Μακεδονίας, 2015) Michailidis, Vasilios; Μιχαηλίδης, Βασίλειος; Mastorocostas, Paris; Μαστοροκώστας, Πάρις; Σχολή Τεχνολογικών Εφαρμογών, Τμήμα Μηχανικών Πληροφορικής Τ.Ε.; Master’s Degree in Communication and Information SystemsThe term cluster analysis does not identify a particular statistical method or model, as do discriminant analysis, factor analysis, and regression. You often don't have to make any assumptions about the underlying distribution of the data. Using cluster analysis, you can also form groups of related variables, similar to what you do in factor analysis. There are numerous ways you can sort cases into groups. The choice of a method depends on, among other things, the size of the data file. Methods commonly used for small data sets are impractical for data files with thousands of cases. In this master dissertation the fuzzy c-means algorithm will be analyzed and modified. The drawback of the algorithm is that it should be paid by the user number clusters. So optimal grouping is achieved by using the validity index according to M.Y. Chen and D.A. Linkens, which is discussed in Chapter 4. Chapter 5 presents a simulation for various data (FCM Data, Data of nonlinear functions). The algorithms have been implemented in the Matlab programming environment.Τεκμήριο A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation(2008-10) Mastorocostas, Paris; Varsamis, Dimitris; Hilas, Constantinos; Mastorocostas, ConstantinosThis 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.Τεκμήριο A Method for Simulating Digital Circuits for Evolutionary Optimization(2014-12) Kazarlis, Spyros; Kalomiros, John; Mastorocostas, Paris; Petridis, Vassilios; Balouktsis, Anastasios; Kalaitzis, Vassilios; Valais, AntoniosThis work presents a method for simulating asynchronous digital circuits, of both combinational and sequential logic, at the gate level. The simulator is going to serve as a fitness function of an Evolutionary Algorithm that will be used for optimal synthesis of digital circuits. Therefore the simulator needs to be simple, fast and reliable. The circuit under evaluation will be given to the simulator in an encoded form resembling DNA. Both the circuit codification method and the simulator are analytically discussed. Results are presented for a number of combinatorial and sequential digital circuits that prove the efficiency of the simulation method.Τεκμήριο An Optimal Bivariate Polynomial Interpolation Basis for the Application of the Evaluation-Interpolation Technique(2014-01-01) Varsamis, Dimitris; Karampetakis, Nicholas; Mastorocostas, ParisA new basis of interpolation points for the special case of the Newton two variable polynomial interpolation problem is proposed. This basis is implemented when the upper bound of the total degree and the degree in each variable is known. It is shown that this new basis under certain conditions (that depends on the degrees of the interpolation polynomial), coincides either with the known triangular/rectangular basis or it is a polygonal basis. In all cases it uses the least interpolation points with further consequences to the complexity of the algorithms that we use.Τεκμήριο A Recurrent Neural Network–based Forecasting System for Telecommunications Call Volume(2013-09-01) Mastorocostas, Paris; Hilas, Constantinos; Varsamis, Dimitris; Dova, StergianiA recurrent neural network–based forecasting system for telecommunications call volume is proposed in this work. In particular, the forecaster is a Block–Diagonal Recurrent Neural Network with internal feedback. Model’s performance is evaluated by use of real–world telecommunications data, where an extensive comparative analysis with a series of existing forecasters is conducted, including both traditional models as well as neural and fuzzy approaches.