Sensitivity of feedforward neural networks to weight errors.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Neural Networks

Background:

• Feedforward layered networks are fundamental in machine learning.
• Adaline elements (adaptive linear neurons) are key components in some neural network architectures.
• Understanding network robustness to internal parameter variations is crucial for reliable AI systems.

Purpose of the Study:

• To analyze the sensitivity of feedforward layered networks of Adaline elements to weight errors.
• To derive an approximation for the probability of output neuron error as a function of weight changes.
• To investigate the impact of network depth and width on error propagation.

Main Methods:

• Analysis of feedforward layered networks composed of Adaline elements.
• Derivation of a mathematical approximation for error probability.
• Examination of sensitivity with respect to percentage changes in weights.

Main Results:

• The probability of output neuron error increases with the number of network layers.
• Error probability is directly correlated with the percentage change in weights.
• Network error probability is largely independent of the number of weights per neuron or neurons per layer when these are large (>=100).

Conclusions:

• Feedforward Adaline networks exhibit sensitivity to weight errors, particularly in deeper architectures.
• The derived approximation provides a quantitative measure of network vulnerability to weight perturbations.
• Robustness in large-scale Adaline networks is maintained concerning neuron and weight counts per layer, but not depth or weight fidelity.