Adaptive Cognitive System Ze

Abstract This article presents an innovative predictive model of the world based on dynamic updating and adaptive filtering of predicates. The system processes elementary units of information - "crumbs" - to build a probabilistic picture of the environment, demonstrating an initial probability of matches of 0.5 and exponential decay to 0.00001 as the number of counters increases. Key mechanisms include: (1) updating significant patterns with PredictIncrement=2, (2) filtering rarely used predicates while maintaining plasticity balance (γ≥0.95), and (3) resource-efficient architecture providing 37-42% computational savings. Experimental results show prediction accuracy of 78-92% for stable flows, adaptation speed of 2-3 seconds, and robustness to 15% noise. A comparative analysis revealed advantages over LSTM networks (3 times less training data) and Markov models (40% higher adaptability). The model exhibits biologically plausible properties, including nonlinear attention distribution and energy efficiency similar to that of the neocortex (40-45%). Application prospects include IoT, cybersecurity and power system management, and further research is aimed at integrating the temporal model and hierarchical organization of patterns. Keywords: artificial intelligence, forecast, filtering, updating, data flow, biologically inspired algorithms, energy-efficient computing