Dynamic Neural Network with Guaranteed Sensitivity to External Influences

Mathematical models are used to represent a vast array of complex processes in engineering, physics, biology, social science, and economics. Model parameters with significant impacts on identification outcomes are ascertained through parametric sensitivity analysis. Authors previously considered an approximation models for systems with uncertain dynamics using a dynamic neural network. The results obtained from studying the problem of predicting the response variable, indicated that this model has a structural flaw. This flaw manifests as an insensitivity of the weight coefficients to external influences, leading to inaccurate predictions. This insensitivity is marked by the minimal contribution of weight coefficient components in the identification process. This paper discusses modifying learning laws to enhance the sensitivity of the weight coefficients to external signals. Through Lyapunov stability analysis, stable algorithms for weight component evolution that minimize identification error were derived.