Artificial Intelligence and Quantum Computing for Advanced Wireless Networks. Savo G. Glisic

x. The following two theorems show that such an intuitive approach has a formal justification. The former theorem proves that function ϕ_w is differentiable.

      Theorem 5.1

      (Differentiability) [1] : Let F_w and G_w be the global transition and the global output functions of a GNN, respectively. If F_w(x, l) and G_w(x, l_N) are continuously differentiable w.r.t. x and w, then ϕ_w is continuously differentiable w.r.t. w (for the proof, see [1]).

Theorem 5.2

      (Backpropagation): Let F_w and G_w be the transition and the output functions of a GNN, respectively, and assume that F_w(x, l) and G_w(x, l_N) are continuously differentiable w.r.t. x and w. Let z(t) be defined by

(5.80)

      Then, the sequence z(T), z(T − 1)…. . converges to a vector z = lim_{t → − ∞} z(t), and the convergence is exponential and independent of the initial state (T). In addition, we have

(5.81)

      where x is the stable state of the GNN (for the proof, see [1]).

The relationship between the gradient defined by Eq. (5.81) and the gradient computed by the Almeida–Pineda algorithm can be easily recognized. The first term on the right‐hand side of Eq. (5.81) represents the contribution to the gradient due to the output function G_w . Backpropagation calculates the first term while it is propagating the derivatives through the layer of the functions g_w (see Chapter 3, Figure 3.10). The second term represents the contribution due to the transition function F_w . In fact, from Eq. (5.80)

If we assume z (T) = ∂e_w(T)/∂o(T) · (∂G_w/∂x(T))(x(T), l_N) and x (t) = x, for t 0 ≤ t ≤ T, it follows that

So, Eq. (5.80) accumulates the ∂e_w(T)/∂x(i) into the variable z. This mechanism corresponds to backpropagating the gradients through the layers containing the f_w units. The learning algorithm is detailed in Table 5.1 [1]. It consists of a main procedure and of two functions: FORWARD and BACKWARD. The function FORWARD takes as input the current set of parameters w and iterates to find the convergent fixed point. The iteration is stopped when ‖x(t) − x(t − 1)‖ is less than a given threshold ε_f according to a given norm ‖ · ‖. The function BACKWARD computes the gradient: system Eq. (5.80) is iterated until ‖z(t − 1) − z(t)‖ is smaller than a threshold ε_b ; then, the gradient is calculated by Eq. (5.81).

      The function FORWARD computes the states, whereas BACKWARD calculates the gradient. The procedure MAIN minimizes the error by calling FORWARD and BACKWARD iteratively.Transition and output function implementations: The implementation of the local output function g_w does not need to fulfill any particular constraint. In GNNs, g_w is a multilayered FNN. On the other hand, the local transition function f_w plays a crucial role in the proposed model, since its implementation determines the number and the existence of the solutions of Eq. (5.74). The assumption underlying GNN is that the design of f_w is such that the global transition function F_w is a contraction map with respect to the state x. In the following, we describe two neural network models that fulfill this purpose using different strategies. These models are based on the nonpositional form described by Eq. (5.76). It can be easily observed that there exist two corresponding models based on the positional form as well.

      1 Linear (nonpositional) GNN. Eq. (5.76) can naturally be implemented by(5.82) where the vector bn ∈ Rs and the matrix An, u ∈ Rs × s are defined by the output of two FNNs, whose parameters correspond to the parameters of the GNN. More precisely, let us call the transition network an FNN that has to generate An, u and the forcing network another FNN that has to generate bn . Let φw : and ρw : be the functions implemented by the transition and the forcing network, respectively. Then, we define(5.83) where μ ∈ (0, 1) and Θ =resize((φw(ln, l(n, u), lu)) hold, and resize (·) denotes the operator that allocates the elements of an s2‐dimensional vector into an s × s matrix. Thus, An, u is obtained by arranging the outputs of the transition network into the square matrix Θ and by multiplication with the factor μ/s ∣ ne[u]∣. On the other hand, bn is just a vector that contains the outputs of the forcing network. In the following, we denote the 1‐norm of a matrix M = {mi, j} as ‖M‖1 = maxj ∑ ∣ mi, j∣ and assume that ‖φw(ln, l(n, u), lu)‖1 ≤ s holds; this can be straightforwardly verified if the output neurons of the transition network use an appropriately bounded activation function, for example, a hyperbolic tangent.

Table 5.1 [1] Learning algorithm.

M ain initialize w; x = Forward(w) ; repeat until (a stopping criterion); return w; end F orward ( w ) Initialize x (0) , t = 0 ; repeat x (t + 1) = Fw ( x (t), l ) ; t = t + 1 ; until ‖ x (t) − x (t − 1)‖ ≤ εf return x (t) ; end Backward ( x , w ) end

      The function FORWARD computes the states, whereas BACKWARD calculates the gradient. The procedure MAIN minimizes the error by calling FORWARD and BACKWARD iteratively.

Note that in this case F_w(x, l) = Ax + b, where b is the vector constructed by stacking all the b_n , and A is a block matrix , with