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Updated: Dec 29, 2025
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Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
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Deterministic learning of hybrid Fuzzy Cognitive Maps and network reduction approaches
Gonzalo Nápoles1, Agnieszka Jastrzębska2, Carlos Mosquera3
1Faculty of Business Economics, Universiteit Hasselt, Belgium; Department of Cognitive Science & Artificial Intelligence, Tilburg University, The Netherlands.
Summary
This study introduces a novel neural approach for hybrid artificial intelligence using Fuzzy Cognitive Maps. It presents a fast, parameterless learning rule and methods for weight relevance and model calibration in dynamic systems.
Area of Science:
- Artificial Intelligence
- Computational Neuroscience
- Systems Engineering
Background:
- Hybrid artificial intelligence integrates human knowledge with data.
- Modeling dynamic systems requires effective integration of these sources.
- Existing methods may lack efficiency or interpretability.
Purpose of the Study:
- To propose a neural perspective for hybrid artificial intelligence in dynamic systems.
- To develop an efficient learning rule for Fuzzy Cognitive Maps.
- To enhance model understanding and calibration.
Main Methods:
- Fuzzy Cognitive Map (FCM) architecture with expert-defined interactions.
- A parameterless learning rule based on Moore-Penrose inverse for weight computation.
- A model for determining weight relevance and two calibration methods.
Main Results:
- A novel FCM architecture for hybrid AI.
- A fast, single-step, parameterless learning rule for weight calculation.
- Methods for assessing weight importance and calibrating the model.
Conclusions:
- The proposed neural approach offers an efficient method for hybrid AI.
- The learning rule and calibration techniques improve dynamic system modeling.
- This work enhances understanding and adaptability of intelligent systems.
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