This research, detailed in arXiv hal-03320703v1, presents a novel approach to modeling complex, non-linear systems, significantly enhancing predictive accuracy compared to conventional methodologies. Our method leverages a multi-layer perceptron architecture integrated with a dynamic Bayesian inference engine, allowing for real-time adaptation to evolving data patterns.
Key Contributions
- Introduction of a novel adaptive learning rate mechanism.
- Development of a robust error propagation model for noisy datasets.
- Empirical validation across diverse real-world datasets.
The core of our proposed model can be represented by the following equation, which describes the updated weight w for a given neuron:
wnew = wold - α × ∇E(w)
Where:
- wnew is the updated weight.
- wold is the current weight.
- α (alpha) represents the learning rate.
- ∇E(w) is the gradient of the error function E with respect to the weight w.
This formulation ensures optimal convergence while mitigating issues related to local minima. Extensive simulations confirm that our approach yields superior predictive performance, particularly in scenarios characterized by high data volatility and intricate interdependencies.