Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis

Empirical Logic is a bio-inspired soft computing method that does not rely on concepts from mathematical logic or Fuzzy Logic. Instead, it emulates a form of intuitive reasoning associated with fast, experience-based decision processes (often referred to as “System 1” in cognitive psychology). The approach has proven useful in a range of practical applications.Using the example of speed control of a DC drive, this paper demonstrates the fundamental procedure for applying Empirical Logic in control engineering. In comparison with fuzzy control approaches, the method exhibits improved dynamic performance and accuracy while requiring significantly less development effort.A second example shows how the collective execution of rules can be utilized for cluster analysis. In contrast to classical hierarchical and partitioning methods (e.g., k-means), the proposed approach does not follow a predefined optimization algorithm. Instead, clusters emerge through self-organized structure formation resulting from the parallel interaction of rules.