A researcher studying neural networks models a simple learning algorithm where each neuron updates its weight by ±15% of the error signal. If the initial error is 40 units and the weight is updated iteratively over 3 steps, increasing in magnitude positively each time, what is the final weight adjustment after three updates applied sequentially?

Tue, 05 May 2026 14:20:35 +0000

Learning Rate Dynamics: Neural Weight Updates Modeled Over Three Iterations In the field of artificial intelligence, neural networks rely heavily on how weights evolve during training. A compelling case arises when modeling weight adjustments using a consistent biasing update rule—such as adjusting weights by ±15% of the current error signal. This approach reflects a simple yet insightful learning mechanism that researchers sometimes adopt for illustrative or optimization studies. In this scenario, consider a researcher investigating neural network models. The initial weight error is measured at 40 units. The learning mechanism dictates that at each step, the neuron’s weight is updated by ±15% of the prevailing error signal, with the direction positive (i.e., weight increases) in every update. Over three sequential iterations, the weight evolves as follows: - Initial error: 40 units
- Update step 1:
Weight adjustment = +15% of 40 = 0.15 × 40 = 6
New weight value: starts at 0 (assumed reset) → updated by +6 → weight = 6