A5075878748- Cognitive Computing and Networks
- Cognitive Science and Mapping
- Multi-Criteria Decision Making
- Neural Networks and Applications
- Music and Audio Processing
- Rough Sets and Fuzzy Logic
- Fuzzy Logic and Control Systems
- Anomaly Detection Techniques and Applications
- Music Technology and Sound Studies
- Time Series Analysis and Forecasting
- Semantic Web and Ontologies
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Advanced Computational Techniques and Applications
- Face and Expression Recognition
- Image Processing and 3D Reconstruction
- Image Retrieval and Classification Techniques
- Handwritten Text Recognition Techniques
- Industrial Vision Systems and Defect Detection
- Natural Language Processing Techniques
- Tactile and Sensory Interactions
- Image and Object Detection Techniques
- Advanced Algebra and Logic
- Fuzzy Systems and Optimization
- Speech and Audio Processing
Warsaw University of Technology
2015-2024
University of Information Technology and Management in Rzeszow
2021-2024
Vilnius University
2014-2020
University of Białystok
2010-2018
Bialystok University of Technology
2015-2017
The University of Western Australia
2016
Warsaw University of Life Sciences
2016
Silesian University of Technology
2016
Khalifa University of Science and Technology
2016
Klaipėda University
2016
This study elaborates on a comprehensive design methodology of fuzzy cognitive maps (FCMs). Here, the are regarded as modeling vehicle time series. It is apparent that whereas series predominantly numeric, FCMs abstract constructs operating at level entities referred to concepts and represented by individual nodes map. We introduce mechanism represent numeric in terms information granules constructed space amplitude change series, which, turn, gives rise collection forming corresponding...
One drawback of using the existing one-step forecasting models for long-term time series prediction is cumulative errors caused by iterations. In order to overcome this shortcoming, article proposes a trend-fuzzy-granulation-based adaptive fuzzy cognitive map (FCM) forecasting. Different from original FCM-based models, class trend information granules built represent trend, fluctuation range, and persistence various segments series, which are more instrumental comprehensive than simple...
Fuzzy cognitive maps (FCMs) form a class of graph-oriented fuzzy models describing causal relationships among concepts. In this study, we augment these by introducing their generalization coming in the granular FCMs. contrast with FCMs, connections between nodes (states) are described information granules, especially intervals and sets. Key scenarios which (and FCMs) arise presented order to offer compelling rationale behind formation such models. context system modeling, show that...
Fuzzy Cognitive Maps are recognized knowledge modeling tool. FCMs visualized with directed graphs. Nodes represent information, edges relations within information. The core element of each Map is weights matrix, which contains evaluations connections between map's nodes. Typically, matrix constructed by experts. can be also reconstructed in an unmanned mode. In this article authors present their own, new approach to time series Maps. Developed methodology joins reconstruction procedure...
This paper presents a time-series classification method based on fuzzy cognitive maps. We advocate that maps provide sound representation of time series, and we can construct mechanism them. The classifier has to distinguish constructed for series belonging different classes. proposed procedure evaluates similarity maps, it is done by comparing weight matrices the same set concepts. A matrix describes relationships between concepts in map. Concepts represent underlying data, because they are...
This article presents a comprehensive approach for time-series classification. The proposed model employs fuzzy cognitive map (FCM) as classification engine. Preprocessed input data feed the employed FCM. Map responses, after postprocessing procedure, are used in calculation of final decision. staged using moving-window technique to capture time flow training procedure. We use backward error propagation algorithm compute required hyperparameters. Four hyperparameters require tuning. Two...
The task of identifying native and foreign elements rejecting ones in the pattern recognition problem is discussed this paper. Such a nonstandard aspect recognition, which rarely present research. In paper, ensembles support vector machines solving two-classes one-class problems are employed as classification tools basic for elements. Evaluation quality rejection methods proposed paper finally some experiments performed order to illustrate acquainted terms methods.
A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of nodes (edges) represent casual relationships between items associated with nodes. This has been applied to solve various modeling tasks including forecasting time series. In original FCM-based model, causal among concepts FCM remain unchanged. However, may change in time. Therefore, we propose new learning method for training an resulting adaptive which consists several sub-FCMs. It can...
Fuzzy cognitive maps (FCMs) are directed graphs with multiple nodes, making them well-suited for addressing multivariate time series (MTS) forecasting problems. When MTS, it is crucial to treat each vector of the MTS as a whole, considering both causalities between different variables at timepoint (spatial relationship) and historical vectors future (temporal relationship). Existing FCM-based models often fail whole do not distinctly reflect temporal relationship spatial in MTS. To address...