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Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster
1Chair of Business Administration, in Particular Planning, Innovation and Founding, Brandenburg University of Technology Cottbus-Senftenberg, Erich-Weinert-Straße 1, D-03046 Cottbus, Germany.
Biomimetics (Basel, Switzerland)
|June 25, 2026
Summary
Empirical logic (EL) offers intuitive, experience-based decision-making. This paper showcases EL applications in control engineering and cluster analysis, demonstrating its practical use with accessible software tools.
Area of Science:
- Soft computing
- Artificial intelligence
- Bio-inspired algorithms
Background:
- Empirical Logic (EL) is a bio-inspired soft computing method for rule-based decision-making.
- Existing research focuses on EL's theory, with less attention on practical application and accessibility.
Purpose of the Study:
- To bridge the gap between EL theory and practice.
- To demonstrate EL's applicability in control engineering and cluster analysis.
- To highlight the availability of EL software for experimentation.
Main Methods:
- Demonstrated EL for DC drive speed control.
- Introduced a novel cluster analysis approach using EL rule interactions.
- Utilized Maple and Python for EL software prototypes.
Main Results:
- EL achieved competitive dynamic performance in DC drive control with few intuitive rules.
- A new cluster analysis method emerged from collective EL rule interactions.
- Publicly accessible software facilitates EL experimentation and deployment.
Conclusions:
- Empirical Logic is practically applicable in diverse domains like control and clustering.
- Accessible software tools accelerate the adoption of EL in applied soft computing.
- This work supports the transition of EL from concept to practical deployment.
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