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Adaptive Multi-Layer Random Generator: Toward Self-Regulating Pseudorandomness | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 اسفند 1404 | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2026.12518.887 | ||
| نویسنده | ||
| Ali Bazghandi* | ||
| Faculty of Computer and IT Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| تاریخ دریافت: 04 آبان 1404، تاریخ بازنگری: 30 بهمن 1404، تاریخ پذیرش: 04 اسفند 1404 | ||
| چکیده | ||
| Background and Objectives: Random number generation is essential in simulation, cryptography, and statistical modeling. Classical PRNGs such as the Linear Congruential Generator and Mersenne Twister are efficient but exhibit predictability and correlation. Newer families like PCG and BRG improve statistical balance yet remain static after initialization, while chaotic and neural methods face reproducibility and stability issues. To overcome these limits, we propose the Adaptive Multi-Layer Random Generator (AMLRG), designed to deliver self-regulating pseudorandomness through adaptive feedback and hybrid entropy sources. Methods: AMLRG combines three layers: (i) an index generator based on linear, logistic, or chaotic processes, (ii) a uniform distribution module, and (iii) an adaptive feedback system that tunes parameters in real time. Online diagnostics—Kolmogorov–Smirnov tests, autocorrelation analysis, and Shannon entropy—direct dynamic adjustment. Implemented in Python, the system produces binary streams and diagnostic plots. Evaluation involved ablation studies (removing feedback, switching, or stratification), comparison with LCG, PCG, BRG, and logistic-only baselines, and validation using Dieharder, TestU01, and NIST SP 800-22. Results: AMLRG produced lower KS distances, near-zero autocorrelation, and entropy close to theoretical maxima, outperforming all baselines. Ablation confirmed the contribution of each layer to statistical quality. Results show stable behavior across 200,000 values, with speed comparable to PCG but greater adaptability. Conclusion: AMLRG introduces dynamic correction that improves independence and uniformity in pseudorandom sequences. Its layered architecture suits engineering simulations, adaptive systems, and security-sensitive statistical preprocessing. Future work will target non-uniform distributions, GPU acceleration, and hardware implementation. | ||
| کلیدواژهها | ||
| Entropy Correction؛ Statistical Testing؛ Kolmogorov–Smirnov Analysis؛ Autocorrelation Metrics؛ Throughput Optimization | ||
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آمار تعداد مشاهده مقاله: 3 |
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