Verimli ve etkili öğrenme için rezervuar hesaplama

Resource-efficient modeling of nonlinear dynamical systems is vital for embedded sensing, edge analytics, and neuromorphic hardware. Reservoir Computing (RC) reduces training overhead in recurrent neural networks and transformers by fixing internal weights and training only a linear read-out. However, classical Echo State Networks (sRC) scale capacity by increasing reservoir size, which inflates memory and energy costs. This thesis addresses that bottleneck by improving the quality, rather than the quantity, of reservoir units. We propose the Higher-Order Augmented Reservoir Computer (haRC), which augments reservoir with a subset of multiplicative monomials of co-temporal activations. This polynomial feature space enhances nonlinear expressivity while keeping the reservoir compact. On chaotic benchmarks such as Lorenz and Mackey–Glass, haRC achieves the same or better accuracy with fewer parameters than baseline sRC models.We propose two improved models: Selective haRC, which ranks reservoir units by variance and covariance to discard redundant interactions, and Sparse haRC, which integrates controlled sparsity masks to reduce memory and compute load. Comprehensive experiments evaluate memorization and forecasting tasks under equal total and tunable parameter settings across different sparsity levels. haRC's consistently outperform sRC across all sparsity regimes, with the advantage growing in high sparsity. Noise robustness tests inject Gaussian noise into the reservoir. Selective haRC maintains or exceeds the performance of sRC in terms of root-mean-squared error and variance with 12% of the reservoir units sRC has. Demonstrating up to 120 times memory efficiency, higher-order augmentation emerges as a promising approach for ultra-lightweight sequence learners suited for microcontrollers, FPGA overlays, and in-memory compute systems.