Control of Multistability in an Erbium-Doped Fiber Laser by an Artificial Neural Network: A Numerical Approach

Abstract

A recurrent wavelet first-order neural network (RWFONN) is proposed to select a desired attractor in a multistable erbium-doped fiber laser (EDFL). A filtered error algorithm is used to classify coexisting EDFL states and train RWFONN. The design of the intracavity laser power controller is developed according to the RWFONN states with the block control linearization technique and the super-twisting control algorithm. Closed-loop stability analysis is performed using the boundedness of synaptic weights. The efficiency of the control method is demonstrated through numerical simulations.

Implementation of Logic Gates in an Erbium-Doped Fiber Laser (EDFL): Numerical and Experimental Analysis

“…To characterize the response of the EDFL to logic gate generation, we need to define a reference signal I d that represents the bias voltage that is injected into the pumping process, as shown in Equation (5). Consider an input signal I d defined as the sum of two digital decorrelated signals I d = I 1 + I 2 .…”

“…To perform numerical simulation, the equations described in Equation ( 1) are normalized to obtain Equation ( 4). The normalization procedure and further details can be found in the appendix of reference [5,28], and the system coefficients are described in Table 1.…”

“…In optical fibers doped with rare earths (also called laser fibers), absorption processes take place, both spontaneous and stimulated emission, when excited with certain energies [1][2][3][4][5]. These characteristics give this type of fibers the necessary properties for their use as optical amplifiers, lasers, sensors, to name a few.…”

A Recurrent Neural Network for Identifying Multiple Chaotic Systems

“…There are recent works in which artificial Recurrent Wavelet First-Order Neural Networks (RWFONN) have been used to identify and control different dynamical systems. This is due to their simple structure, good identification capability, and low computational cost [17,21]. The general structure of this kind of RWFONN is given by…”

“…In particular, regarding the online algorithms, Recurrent Wavelet First-Order Neural Networks (RWFONNs) have been mainly used to implement neural control of electrical machines and emulated energy storage systems, to identify and control electrical systems, robotic manipulators, and crewless aerial vehicles [19,20], and for the identification and control of multi-stable systems [21]. Magallón and coworkers, in turn, have presented an RWFONN that can identify systems based on the jerk equation (an Unstable Dissipative System of type I (UDS I) [22] and a memristive system without equilibrium points [23]), where the values and structure in the network are the same for both systems [17].…”