where z represents the weights w and biases b of the network, the product JT k Jk is referred to as Hessian matrix approximation for non-linear least square optimization [39], Jk is the Jacobian matrix that contains first derivatives of the network MSE with respect to the weights and biases [40], I is the identity matrix [41], λk is the non-negative learning parameter [42] and the symbol T { \displaystyle ˆ{\mathsf {T}}}denotes the matrix transpose [43].