WSEAS Transactions on Business and Economics
Print ISSN: 1109-9526, E-ISSN: 2224-2899
Volume 23, 2026
A Neuro-Symbolic Edge Stack for Fragile Economies – Binary Cellular
Neural Networks and Auto-Mined ASP Rules for Robust Econometrics
in the Democratic Republic of Congo
Authors: , , , , ,
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Abstract: Forecasting macro- and microeconomic trends is especially difficult in data-scarce settings such as the Democratic Republic of the Congo (DRC). Standard deep learning models, LSTMs, temporal CNNs, and Transformers typically require clean, synchronized time-series data and extensive GPU training. Yet, they offer limited transparency to central bank analysts and policymakers. We propose dCNN-E(ASP), a fully analytical neuro-symbolic framework that combines a binary Cellular Neural Network reservoir with a self-growing Answer Set Programming rulebook. Training requires only two closed-form matrix inversions, enabling realtime inference on a $30 Raspberry Pi and robustness to missing or noisy data. Across three Congolese applications, headline inflation, hydropower grid balancing, and cross-border copper flows retrospective backtests achieve 12–20% lower MAPE than tuned benchmarks. The method also provides clause-level explanations (e.g., a fuel-price shock within 14 days triggers a maize-price surge) and includes tutorial prose and pseudocode for easy local replication without GPUs.
Keywords:
Neuro-Symbolic Forecasting, Binary Cellular Neural Networks (dCNN), Answer-Set Programming
(ASP), Reservoir Computing at the Edge, Energy-Efficient Inference, Economic Time-Series (DR
Congo), Explainable Artificial Intelligence (XAI), Low-Power Embedded AI
Pages: 255-276
DOI: 10.37394/23207.2026.23.20