Adaptive Cognitive System Ze
DOI:
https://doi.org/10.5281/zenodo.15309162Keywords:
Artificial Intelligence, Forecast, Filtering, Updating, Data Flow, Biologically Inspired Algorithms, Energy-efficient ComputingAbstract
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.
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