as. dr. Goran Andonovski

@article{ANDONOVSKI2026114058,

title = {Advancements in data-driven evolving fuzzy and neuro-fuzzy control: A comprehensive survey},

author = {Goran Andonovski and Daniel Leite and Radu-Emil Precup and Fernando Gomide and Mahardhika Pratama and Igor \v{S}krjanc},

url = {https://www.sciencedirect.com/science/article/pii/S1568494625013717},

doi = {https://doi.org/10.1016/j.asoc.2025.114058},

issn = {1568-4946},

year  = {2026},

date = {2026-01-01},

journal = {Applied Soft Computing},

volume = {186},

pages = {114058},

abstract = {In an era of increasing system complexity and growing demands for autonomy and efficiency, control systems must continuously adapt to dynamic and uncertain environments. This study presents a comprehensive survey of evolving fuzzy and neuro-fuzzy controllers, with emphasis on data-driven control systems that adapt in real time in both structure and parameters. As the demand for adaptive and flexible control solutions grows alongside the increasing complexity of systems, evolving model-free and model-based fuzzy, neural, and neuro-fuzzy controllers have emerged as robust approaches, allowing models and controllers to integrate new patterns from data streams. Incremental machine learning methods enable control systems to autonomously detect and track new behaviors, improving their effectiveness in time-varying and unknown environments. Based on a rigorous bibliometric analysis using the Web of Science database, 2760 related papers were identified of which 97 were manually selected for detailed review due to their direct relevance to closed-loop evolving fuzzy or neuro-fuzzy control systems. These papers cover a wide range of methods, including basic parameter tuning, adaptive gain scheduling, and structural modifications grounded in constrained optimization and Lyapunov stability analysis. Such advances mark significant progress in the control of unknown, time-varying systems, with the surveyed literature demonstrating promising results in various applications. The abstracted findings reveal an increase in publications since 2013, confirming the relevance of evolving control in engineering. This review provides a comprehensive analysis of methodologies and achievements in the field, highlighting emerging trends, challenges, and research directions within evolving data-driven control. The novelty of this study lies in its focus on the structural evolution of controllers under real-time constraints, consolidating incremental machine learning for partition-based closed-loop architectures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{machines12080508,

title = {The Development of Simulation and Optimisation Tools with an Intuitive User Interface to Improve the Operation of Electric Arc Furnaces},

author = {Simon Toma\v{z}i\v{c} and Igor \v{S}krjanc and Goran Andonovski and Vito Logar},

url = {https://www.mdpi.com/2075-1702/12/8/508?utm_source=chatgpt.com},

doi = {10.3390/machines12080508},

issn = {2075-1702},

year  = {2024},

date = {2024-08-05},

urldate = {2024-08-05},

journal = {Machines},

volume = {12},

number = {508},

issue = {8},

pages = {508},

abstract = {The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT) communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{met12050816,

title = {Data-Driven Modelling and Optimization of Energy Consumption in EAF},

author = {Simon Toma\v{z}i\v{c} and Goran Andonovski and Igor \v{S}krjanc and Vito Logar},

url = {https://www.mdpi.com/2075-4701/12/5/816},

doi = {10.3390/met12050816},

issn = {2075-4701},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Metals},

volume = {12},

number = {5},

issue = {816},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Leite2020a,

title = {Optimal rule-based granular systems from data streams},

author = {Daniel Leite and Goran Andonovski and Igor \v{S}krjanc and Fernando Gomide},

doi = {10.1109/TFUZZ.2019.2911493},

issn = {19410034},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Transactions on Fuzzy Systems},

volume = {28},

number = {3},

pages = {583--596},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Skrjanc2017a,

title = {Evolving cloud-based system for the recognition of drivers' actions},

author = { Igor \v{S}krjanc and Goran Andonovski and Agapito Ledezma and Oscar Sipele and Jose Antonio Iglesias and Araceli Sanchis},

issn = {09574174},

year  = {2018},

date = {2018-01-01},

journal = {Expert Systems with Applications},

volume = {99},

pages = {231--238},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Andonovski2017b,

title = {Evolving model identification for process monitoring and prediction of non-linear systems},

author = { Goran Andonovski and Ga\v{s}per Mu\v{s}i\v{c} and Sa\v{s}o Bla\v{z}i\v{c} and Igor \v{S}krjanc},

issn = {09521976},

year  = {2017},

date = {2017-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {68},

number = {October},

pages = {214--221},

publisher = {Elsevier Ltd},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Andonovski2016b,

title = {On-line Evolving Cloud-based Model Identification for Production Control},

author = { Goran Andonovski and Ga\v{s}per Mu\v{s}i\v{c} and Saso Bla\v{z}i\v{c} and Igor \v{S}krjanc},

issn = {24058963},

year  = {2016},

date = {2016-01-01},

journal = {IFAC-PapersOnLine},

volume = {49},

number = {5},

pages = {79--84},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Andonovski_RECCo_PHE_2016,

title = {A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant},

author = { Goran Andonovski and Plamen Angelov and Sa\v{s}o Bla\v{z}i\v{c} and Igor \v{S}krjanc},

issn = {15684946},

year  = {2016},

date = {2016-01-01},

journal = {Applied Soft Computing},

volume = {48},

pages = {29--38},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ANDONOVSKI2026114058,

title = {Advancements in data-driven evolving fuzzy and neuro-fuzzy control: A comprehensive survey},

author = {Goran Andonovski and Daniel Leite and Radu-Emil Precup and Fernando Gomide and Mahardhika Pratama and Igor \v{S}krjanc},

url = {https://www.sciencedirect.com/science/article/pii/S1568494625013717},

doi = {https://doi.org/10.1016/j.asoc.2025.114058},

issn = {1568-4946},

year  = {2026},

date = {2026-01-01},

journal = {Applied Soft Computing},

volume = {186},

pages = {114058},

abstract = {In an era of increasing system complexity and growing demands for autonomy and efficiency, control systems must continuously adapt to dynamic and uncertain environments. This study presents a comprehensive survey of evolving fuzzy and neuro-fuzzy controllers, with emphasis on data-driven control systems that adapt in real time in both structure and parameters. As the demand for adaptive and flexible control solutions grows alongside the increasing complexity of systems, evolving model-free and model-based fuzzy, neural, and neuro-fuzzy controllers have emerged as robust approaches, allowing models and controllers to integrate new patterns from data streams. Incremental machine learning methods enable control systems to autonomously detect and track new behaviors, improving their effectiveness in time-varying and unknown environments. Based on a rigorous bibliometric analysis using the Web of Science database, 2760 related papers were identified of which 97 were manually selected for detailed review due to their direct relevance to closed-loop evolving fuzzy or neuro-fuzzy control systems. These papers cover a wide range of methods, including basic parameter tuning, adaptive gain scheduling, and structural modifications grounded in constrained optimization and Lyapunov stability analysis. Such advances mark significant progress in the control of unknown, time-varying systems, with the surveyed literature demonstrating promising results in various applications. The abstracted findings reveal an increase in publications since 2013, confirming the relevance of evolving control in engineering. This review provides a comprehensive analysis of methodologies and achievements in the field, highlighting emerging trends, challenges, and research directions within evolving data-driven control. The novelty of this study lies in its focus on the structural evolution of controllers under real-time constraints, consolidating incremental machine learning for partition-based closed-loop architectures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{machines12080508,

title = {The Development of Simulation and Optimisation Tools with an Intuitive User Interface to Improve the Operation of Electric Arc Furnaces},

author = {Simon Toma\v{z}i\v{c} and Igor \v{S}krjanc and Goran Andonovski and Vito Logar},

url = {https://www.mdpi.com/2075-1702/12/8/508?utm_source=chatgpt.com},

doi = {10.3390/machines12080508},

issn = {2075-1702},

year  = {2024},

date = {2024-08-05},

urldate = {2024-08-05},

journal = {Machines},

volume = {12},

number = {508},

issue = {8},

pages = {508},

abstract = {The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT) communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{met12050816,

title = {Data-Driven Modelling and Optimization of Energy Consumption in EAF},

author = {Simon Toma\v{z}i\v{c} and Goran Andonovski and Igor \v{S}krjanc and Vito Logar},

url = {https://www.mdpi.com/2075-4701/12/5/816},

doi = {10.3390/met12050816},

issn = {2075-4701},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Metals},

volume = {12},

number = {5},

issue = {816},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Leite2020a,

title = {Optimal rule-based granular systems from data streams},

author = {Daniel Leite and Goran Andonovski and Igor \v{S}krjanc and Fernando Gomide},

doi = {10.1109/TFUZZ.2019.2911493},

issn = {19410034},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Transactions on Fuzzy Systems},

volume = {28},

number = {3},

pages = {583--596},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Skrjanc2017a,

title = {Evolving cloud-based system for the recognition of drivers' actions},

author = { Igor \v{S}krjanc and Goran Andonovski and Agapito Ledezma and Oscar Sipele and Jose Antonio Iglesias and Araceli Sanchis},

issn = {09574174},

year  = {2018},

date = {2018-01-01},

journal = {Expert Systems with Applications},

volume = {99},

pages = {231--238},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Andonovski2017b,

title = {Evolving model identification for process monitoring and prediction of non-linear systems},

author = { Goran Andonovski and Ga\v{s}per Mu\v{s}i\v{c} and Sa\v{s}o Bla\v{z}i\v{c} and Igor \v{S}krjanc},

issn = {09521976},

year  = {2017},

date = {2017-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {68},

number = {October},

pages = {214--221},

publisher = {Elsevier Ltd},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Andonovski2016b,

title = {On-line Evolving Cloud-based Model Identification for Production Control},

author = { Goran Andonovski and Ga\v{s}per Mu\v{s}i\v{c} and Saso Bla\v{z}i\v{c} and Igor \v{S}krjanc},

issn = {24058963},

year  = {2016},

date = {2016-01-01},

journal = {IFAC-PapersOnLine},

volume = {49},

number = {5},

pages = {79--84},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Andonovski_RECCo_PHE_2016,

title = {A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant},

author = { Goran Andonovski and Plamen Angelov and Sa\v{s}o Bla\v{z}i\v{c} and Igor \v{S}krjanc},

issn = {15684946},

year  = {2016},

date = {2016-01-01},

journal = {Applied Soft Computing},

volume = {48},

pages = {29--38},

keywords = {},

pubstate = {published},

tppubtype = {article}

}