Πλοήγηση ανά Συγγραφέα "Mastorocostas, P. A."
Τώρα δείχνει 1 - 5 από 5
- Αποτελέσματα ανά σελίδα
- Επιλογές ταξινόμησης
Τεκμήριο A dynamic fuzzy model for processing lung sounds(2007-03) Mastorocostas, P. A.; Varsamis, D. N.; Mastorocostas, C. A.; Hilas, C. S.A dynamic fuzzy filter is proposed that performs the task of separation of lung sounds obtained from patients with pulmonary pathology. The consequent parts of the fuzzy rules are dynamic, consisting of block-diagonal recurrent neural networks. The lung sound category of coarse crackles is examined, and a comparative analysis with other fuzzy and neural filters is conducted.Τεκμήριο A Simulated Annealing-based Learning Algorithm for Block-Diagonal Recurrent Neural Networks(2006) Mastorocostas, P. A.; Varsamis, D. N.; Mastorocostas, C. A.The RPROP algorithm was originally developed in [5] for static networks and constitutes one of the best performing first order learning methods for neural networks [6]. However, in RPROP the problem of poor convergence to local minima, faced by all gradient descent-based methods, is not fully eliminated. Hence, in an attempt to alleviate this drawback, a combination of RPROP with the global search technique of Simulated Annealing (SA) was introduced in [7]. The resulted algorithm, named SARPROP, was proved to be an efficient learning method for static neural networks. A fast and efficient training method for block-diagonal recurrent fuzzy neural networks is proposed. The method modifies the Simulated Annealing RPROP algorithm, originally developed for static models, in order to be applied to dynamic systems. A comparative analysis with a series of algorithms and recurrent models is given, indicating the effectiveness of the proposed learning approach.Τεκμήριο Spectral-spatial classification of remote sensing images using a region-based GeneSIS Segmentation algorithm(2014) Mylonas, S. K.; Stavrakoudis, D. G.; Theocharis, J. B.; Mastorocostas, P. A.This paper proposes a spectral-spatial classification scheme for the classification of remotely sensed images, based on a new version of the recently proposed Genetic Sequential Image Segmentation (GeneSIS). GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic algorithm-based object extraction method. In the previous version of GeneSIS, the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels. In the present proposal, a watershed-driven fine segmentation map is initially obtained which serves as the basis for the upcoming GeneSIS segmentation. Our objective is to enhance the flexibility of the algorithm in extracting more flexible object shapes and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS procedure. Accordingly, the previously proposed fitness components are redefined in order to accommodate with the new structural components. In this work, the set of fuzzy membership maps required by GeneSIS are obtained via an unsupervised fuzzy clustering. The final classification result is obtained by combining the results from the unsupervised segmentation and the pixel-wise SVM classifier via majority voting. The validity of the proposed method is demonstrated on the land cover classification of a high-resolution hyperspectral image.Τεκμήριο A telecommunications call volume forecasting system based on a recurrent fuzzy neural network(2013) Mastorocostas, P. A.; Hilas, C. S.; Varsamis, D. N.; Dova, S. C.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.Τεκμήριο A TSK-based fuzzy system for telecommunications time-series forecasting(2012) Mastorocostas, P. A.; Hilas, C. S.; Dova, S. C.; Varsamis, D. N.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.