@inproceedings{MTMT:34043590,
	title = {Machine learning and fuzzy cognitive maps in a hybrid approach toward freeway on-ramp traffic control},
	url = {https://m2.mtmt.hu/api/publication/34043590},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Kóczy, T. László},
	booktitle = {IEEE 17th International Symposium on Applied Computational Intelligence and Informatics SACI 2023 : Proceedings},
	doi = {10.1109/SACI58269.2023.10158585},
	unique-id = {34043590},
	year = {2023},
	pages = {000587-000592},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:32545090,
	title = {Global stability of fuzzy cognitive maps},
	url = {https://m2.mtmt.hu/api/publication/32545090},
	author = {Harmati, István Árpád and Hatwágner, F. Miklós and Kóczy, T. László},
	doi = {10.1007/s00521-021-06742-9},
	journal-iso = {NEURAL COMPUT APPL},
	journal = {NEURAL COMPUTING & APPLICATIONS},
	volume = {35},
	unique-id = {32545090},
	issn = {0941-0643},
	year = {2023},
	eissn = {1433-3058},
	pages = {7283-7295},
	orcid-numbers = {Harmati, István Árpád/0000-0002-0915-9718; Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inproceedings{MTMT:33049054,
	title = {Fuzzy System-Based Solutions for Traffic Control in Freeway Networks Toward Sustainable Improvement},
	url = {https://m2.mtmt.hu/api/publication/33049054},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Kóczy, T. László},
	booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems: 19th International Conference, IPMU 2022 : Proceedings, Part II},
	doi = {10.1007/978-3-031-08974-9_23},
	volume = {1602 CCIS},
	unique-id = {33049054},
	year = {2022},
	pages = {288-305},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:33272027,
	title = {A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies},
	url = {https://m2.mtmt.hu/api/publication/33272027},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Kóczy, T. László},
	doi = {10.3390/math10214139},
	journal-iso = {MATHEMATICS-BASEL},
	journal = {MATHEMATICS},
	volume = {10},
	unique-id = {33272027},
	abstract = {Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control.},
	year = {2022},
	eissn = {2227-7390},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inproceedings{MTMT:32678952,
	title = {Novel Methods of FCM Model Reduction},
	url = {https://m2.mtmt.hu/api/publication/32678952},
	author = {Hatwágner, F. Miklós and Kóczy, T. László},
	booktitle = {Computational Intelligence and Mathematics for Tackling Complex Problems 2},
	doi = {10.1007/978-3-030-88817-6_12},
	unique-id = {32678952},
	year = {2022},
	pages = {101-112},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:32556112,
	title = {A vehicular traffic congestion predictor system using Mamdani fuzzy inference},
	url = {https://m2.mtmt.hu/api/publication/32556112},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Mikulai, Gergely Csaba and Kóczy, T. László},
	doi = {10.52846/stccj.2021.1.2.27},
	journal-iso = {SYST THEORY CONTROL COMP J},
	journal = {SYSTEM THEORY CONTROL AND COMPUTING JOURNAL},
	volume = {1},
	unique-id = {32556112},
	issn = {2668-2966},
	year = {2021},
	eissn = {2810-4099},
	pages = {49-57},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:32537879,
	title = {Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation},
	url = {https://m2.mtmt.hu/api/publication/32537879},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Mikulai, Gergely Csaba and Kóczy, T. László},
	doi = {10.36244/ICJ.2021.3.2},
	journal-iso = {INFOCOMM J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {13},
	unique-id = {32537879},
	issn = {2061-2079},
	abstract = {Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.},
	year = {2021},
	eissn = {2061-2125},
	pages = {14-23},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:31809177,
	title = {Stability of Fixed-Point Values in Reduced Fuzzy Cognitive Map Models},
	url = {https://m2.mtmt.hu/api/publication/31809177},
	author = {Hatwágner, F. Miklós and Kóczy, T. László},
	doi = {10.1007/978-3-030-47124-8_29},
	journal-iso = {STUD FUZZ SOFT COMP},
	journal = {STUDIES IN FUZZINESS AND SOFT COMPUTING},
	volume = {393},
	unique-id = {31809177},
	issn = {1434-9922},
	year = {2021},
	pages = {359-372},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inproceedings{MTMT:32101466,
	title = {An intelligent traffic congestion detection approach based on fuzzy inference system},
	url = {https://m2.mtmt.hu/api/publication/32101466},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Mikulai, Gergely Csaba and Kóczy, T. László},
	booktitle = {15th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2021},
	doi = {10.1109/SACI51354.2021.9465637},
	unique-id = {32101466},
	abstract = {Traffic congestion causes significant economic and social consequences. Instant detection of vehicular traffic breakdown has a pivotal role in intelligent transportation engineering. Common traffic estimators and predictors systems need traffic observations to be classified in their binary-set-nature computation methods which are unable to be an effective base for traffic modeling, since they are defined by precise and deterministic characteristics while traffic is known to be a highly complex and nonlinear system, which may be prescribed by uncertain models containing vague properties. This study aims at applying a new fuzzy inference model for predicting the level of congestion in such heterogeneous and convoluted networks, where the paucity of accurate and real-time data can cause problems in interpreting the whole system state by conventional quantitative techniques. The proposed fuzzy inference model is based on real data extracted from Hungarian network of freeways. As input variables traffic flow and approximate capacity of each segment are considered and level of congestion is regarded as output variable. In the model, a total number of 75 rules were developed on the basis of available datasets, percentile distribution, and experts' judgments. Designed model and analyzing steps are simulated and proven by Matlab fuzzy logic toolbox. The results illustrate correlations and relationships among input variables with predicting the level of congestion based on available resources. Furthermore, performed analyses beside their tractability in dealing with ambiguity and subjectivity are aligned with intelligent traffic modeling purposes in designing traffic breakdown-related alert or early warning systems, infrastructure and services planning, and sustainability development.},
	year = {2021},
	pages = {97-104},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inbook{MTMT:31430826,
	title = {A Novel Approach to Analyze the Behavior of Fuzzy Cognitive Maps},
	url = {https://m2.mtmt.hu/api/publication/31430826},
	author = {Hatwágner, F. Miklós and Kóczy, T. László},
	booktitle = {Kutatási jelentés - Research Report, 2. kötet},
	unique-id = {31430826},
	year = {2020},
	pages = {351-356},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:31251813,
	title = {Classification of plantar foot alterations by fuzzy cognitive maps against multi-layer perceptron neural network},
	url = {https://m2.mtmt.hu/api/publication/31251813},
	author = {Ramirez-Bautista, Julian Andres and Huerta-Ruelas, Jorge Adalberto and Kóczy, T. László and Hatwágner, F. Miklós and Chaparro-Cárdenas, Silvia L. and Hernández-Zavala, Antonio},
	doi = {10.1016/j.bbe.2019.12.008},
	journal-iso = {BIOCYBERN BIOMED ENG},
	journal = {BIOCYBERNETICS AND BIOMEDICAL ENGINEERING},
	volume = {40},
	unique-id = {31251813},
	issn = {0208-5216},
	year = {2020},
	eissn = {0208-5216},
	pages = {404-414},
	orcid-numbers = {Kóczy, T. László/0000-0003-1316-4832; Hatwágner, F. Miklós/0000-0002-5545-2018}
}

@article{MTMT:30326141,
	title = {Reduced Model Investigations Supported by Fuzzy Cognitive Map to Foster Circular Economy},
	url = {https://m2.mtmt.hu/api/publication/30326141},
	author = {Buruzs, Adrienn and Hatwágner, F. Miklós and Kóczy, T. László},
	doi = {10.1007/978-3-030-00485-9_22},
	journal-iso = {STUD COMP INTELLIG},
	journal = {STUDIES IN COMPUTATIONAL INTELLIGENCE},
	volume = {796},
	unique-id = {30326141},
	issn = {1860-949X},
	year = {2019},
	eissn = {1860-9503},
	pages = {191-202},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inbook{MTMT:32749368,
	title = {Banking Applications of FCM Models},
	url = {https://m2.mtmt.hu/api/publication/32749368},
	author = {Hatwágner, F. Miklós and Vastag, Gyula and Niskanen, Vesa A. and Kóczy, T. László},
	booktitle = {Trends in Mathematics and Computational Intelligence},
	doi = {10.1007/978-3-030-00485-9_7},
	unique-id = {32749368},
	year = {2019},
	pages = {61-72},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Vastag, Gyula/0000-0002-6823-3367; Kóczy, T. László/0000-0003-1316-4832}
}

@inproceedings{MTMT:33079543,
	title = {Detection of Human Footprint Alterations by Fuzzy Cognitive Maps Trained with Genetic Algorithm},
	url = {https://m2.mtmt.hu/api/publication/33079543},
	author = {Andres, Ramirez-Bautista Julian and Hernandez-Zavala, Antonio and Huerta-Ruelas, Jorge A. and Hatwágner, F. Miklós and Chaparro-Cardenas, Silvia L. and Kóczy, T. László},
	booktitle = {2018 17TH MEXICAN INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (MICAI 2018)},
	doi = {10.1109/MICAI46078.2018.00013},
	unique-id = {33079543},
	abstract = {Mobility is an important part of our daily life, hence the good health of our lower extremities is essential. Gait analysis using kinetic data along with medical Decision Support System or Computer Aided Diagnosis provide to physicians support in gait disorder detection, the risk of foot ulcerations especially in diabetic patients, leg discrepancy, footprint pathologies, and many other applications in biomedical diagnosis. To increase confidence in the system, it is necessary to use a technique which uses a comprehensive reasoning and provide explanations to discover new relationships and combination of features. The present research is an attempt to assess the viability of investigating human footprint alterations using Fuzzy Cognitive Maps (FCM) combined with a Genetic Algorithm (GA), and it is part for preparation of investigating more efficient algorithms in the future. In the proposed method, GA is used to learn the weight matrix of an FCM model applied to identify alterations in the human footprint. Using historical plantar pressure data obtained by electronic platforms, combined with FCM and optimization algorithm, a promising outcome is presented in the field of Computer-Aided Diagnosis.},
	keywords = {GENETIC ALGORITHM; fuzzy cognitive maps; DISEASE DIAGNOSIS; Computer Science, Artificial Intelligence; plantar alterations},
	year = {2018},
	pages = {32-38},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inproceedings{MTMT:3394825,
	title = {Improved Behavioral Analysis of Fuzzy Cognitive Map Models},
	url = {https://m2.mtmt.hu/api/publication/3394825},
	author = {Hatwágner, F. Miklós and Vastag, Gyula and Niskanen, V A and Kóczy, T. László},
	booktitle = {Artificial Intelligence and Soft Computing},
	doi = {10.1007/978-3-319-91262-2_55},
	unique-id = {3394825},
	abstract = {Fuzzy Cognitive Maps (FCMs) are widely applied for describing the major components of complex systems and their interconnections. The popularity of FCMs is mostly based on their simple system representation, easy model creation and usage, and its decision support capabilities. The preferable way of model construction is based on historical, measured data of the investigated system and a suitable learning technique. Such data are not always available, however. In these cases experts have to define the strength and direction of causal connections among the components of the system, and their decisions are unavoidably affected by more or less subjective elements. Unfortunately, even a small change in the estimated strength may lead to significantly different simulation outcome, which could pose significant decision risks. Therefore, the preliminary exploration of model ‘sensitivity’ to subtle weight modifications is very important to decision makers. This way their attention can be attracted to possible problems. This paper deals with the advanced version of a behavioral analysis. Based on the experiences of the authors, their method is further improved to generate more life-like, slightly modified model versions based on the original one suggested by experts. The details of the method is described, its application and the results are presented by an example of a banking application. The combination of Pareto-fronts and Bacterial Evolutionary Algorithm is a novelty of the approach. © Springer International Publishing AG, part of Springer Nature 2018.},
	year = {2018},
	pages = {630-641},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Vastag, Gyula/0000-0002-6823-3367; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:30478603,
	title = {Two-Stage Learning Based Fuzzy Cognitive Maps Reduction Approach},
	url = {https://m2.mtmt.hu/api/publication/30478603},
	author = {Hatwágner, F. Miklós and Yesil, Engin and Dodurka, M. Furkan and Papageorgiou, Elpiniki and Urbas, Leon and Kóczy, T. László},
	doi = {10.1109/TFUZZ.2018.2793904},
	journal-iso = {IEEE T FUZZY SYST},
	journal = {IEEE TRANSACTIONS ON FUZZY SYSTEMS},
	volume = {26},
	unique-id = {30478603},
	issn = {1063-6706},
	abstract = {In this study, a new two-stage learning based reduction approach for fuzzy cognitive maps (FCM) is introduced in order to reduce the number of concepts. FCM is a graphical modeling technique that follows a reasoning approach similar to the human reasoning and the decision-making process. The FCM model incorporates the available knowledge and expertise in the form of concepts and in the direction and strength of the interactions among concepts. One of the modeling problems of FCMs is that oversized FCM models suffer from interpretability problems. An oversized FCM may contain concepts that are semantically similar and affect the other concepts in a similar way. This new study introduces a two-stage model reduction approach, and both static and dynamic analyses are considered without losing essential information. In the first stage, the number of concepts is reduced by merging similar concepts into clusters, whereas in the second stage the transformation function parameters of concepts are optimized. In order to show the benefit of using the proposed reduction approach, two sets of studies are conducted. First, a huge set of synthetic FCMs are generated, and the results of these statistical analyses are presented via various tables and figures. Subsequently, suggestions to the decision makers are given. Second, experimental studies are also presented to show the decision parameters and procedure for the proposed approach. The results show that after using the concept reduction approach presented in this study, the interpretability of FCM increases with an acceptable amount of information loss in the output concepts.},
	keywords = {Clustering; Computer Science, Artificial Intelligence; Big Bang-Big Crunch (BB-BC) optimization; concept reduction; fuzzy cognitive maps (FCM); BIG CRUNCH OPTIMIZATION},
	year = {2018},
	eissn = {1941-0034},
	pages = {2938-2952},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:27434245,
	title = {A study on solving single stage batch process scheduling problems with an evolutionary algorithm featuring bacterial mutations},
	url = {https://m2.mtmt.hu/api/publication/27434245},
	author = {Hegyháti, Máté and Ősz, Olivér and Hatwágner, F. Miklós},
	doi = {10.1007/978-3-319-91253-0_36},
	journal-iso = {LECT NOTES ARTIF INT},
	journal = {LECTURE NOTES IN ARTIFICIAL INTELLIGENCE},
	volume = {10841 LNAI},
	unique-id = {27434245},
	issn = {0302-9743},
	year = {2018},
	pages = {386-394},
	orcid-numbers = {Hegyháti, Máté/0000-0003-3689-9866; Ősz, Olivér/0000-0001-5306-0451; Hatwágner, F. Miklós/0000-0002-5545-2018}
}

@article{MTMT:3394816,
	title = {On the Existence and Uniqueness of Fixed Points of Fuzzy Cognitive Maps},
	url = {https://m2.mtmt.hu/api/publication/3394816},
	author = {Harmati, István Árpád and Hatwágner, F. Miklós and Kóczy, T. László},
	doi = {10.1007/978-3-319-91473-2_42},
	journal-iso = {COMMUN COMPUT INFORM SCI},
	journal = {COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE},
	volume = {853},
	unique-id = {3394816},
	issn = {1865-0929},
	year = {2018},
	eissn = {1865-0937},
	pages = {490-500},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@CONFERENCE{MTMT:3280851,
	title = {Reduced Model Investigations supported by Fuzzy Cognitive Map to Foster Circular Economy},
	url = {https://m2.mtmt.hu/api/publication/3280851},
	author = {Buruzs, Adrienn and Kóczy, T. László and Hatwágner, F. Miklós},
	booktitle = {9th European Symposium on Computational Intelligence and Mathematics},
	unique-id = {3280851},
	year = {2017},
	pages = {165-175},
	orcid-numbers = {Kóczy, T. László/0000-0003-1316-4832; Hatwágner, F. Miklós/0000-0002-5545-2018}
}

@article{MTMT:3180911,
	title = {A concept reduction approach for fuzzy cognitive map models in decision making and management},
	url = {https://m2.mtmt.hu/api/publication/3180911},
	author = {Elpiniki, I Papageorgiou and Hatwágner, F. Miklós and Buruzs, Adrienn and Kóczy, T. László},
	doi = {10.1016/j.neucom.2016.11.060},
	journal-iso = {NEUROCOMPUTING},
	journal = {NEUROCOMPUTING},
	volume = {232},
	unique-id = {3180911},
	issn = {0925-2312},
	keywords = {modelling; Clustering; *Decision Making; Waste management; fuzzy cognitive maps},
	year = {2017},
	eissn = {1872-8286},
	pages = {16-33},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}