Fig. 2
From: A dynamic neural network model for predicting risk of Zika in real time
Schematic of NARX network with dx input and dy output delays: Each neuron produces a single output based on several real-valued inputs to that neuron by forming a linear combination using its input weights and sometimes passing the output through a nonlinear activation function: \( \mathbf{z}=\boldsymbol{\upvarphi} \left(\sum \limits_{\mathbf{i}=\mathbf{1}}^{\mathbf{n}}{\mathbf{w}}_{\mathbf{i}}{\mathbf{u}}_{\mathbf{i}}+\mathbf{b}\right)=\boldsymbol{\upvarphi} \left({\mathbf{w}}^{\mathbf{T}}\mathbf{x}+\mathbf{b}\right) \), where w denotes the vector of weights, u is the vector of inputs, b is the bias, and φ is a linear or nonlinear activation function (e.g., linear, sigmoid, and hyperbolic tangent [82])