Optimization of photonic reservoir computing using short internal cavity laser

Abstract

With the advent of the high-speed information age and the explosive of information, higher requirements are placed on the information processing speed. In recent years, researchers have carried out a lot of research on delay-based reservoir computing (RC) systems. Meanwhile, the improvement of information processing rate mainly revolves around the replacement of nonlinear nodes in the system. However, as the most frequently used distributed feedback semiconductor (DFB) laser, many researchers only use ordinary commercial DFB products for research, and they have not noticed the improvement of RC performance caused by changes in internal parameters of laser. With the development of photonic integration technology, the processing technology of DFB is more mature, so that the size of DFB can fabricate in the range of 100 μm-1 mm when it still generates laser, and the photon lifetime of the laser will also change. Since the shorter photon lifetime in the laser leads to a faster dynamic response, which has the potential to process higher rate of information in the RC system. According to the laser rate equation (Lang-Kobayashi), changing the internal cavity length will affect the feedback strength, injection strength and other parameters required for the laser to enter each dynamic state, which in turn affects the parameter space required for the RC system to exhibit high performance. Based on this, we studied the relationship between the internal cavity length (120 μm-900 μm) and the information processing rate of the RC system. In addition, the influence of different internal cavity lengths on the parameter space of the RC system is analyzed. The results show that when the internal cavity length is in the range of 120 μm to 171 μm, the system can achieve 20 Gbps low-error information processing; It is worth noting that when the internal cavity length is reduced from 600 μm to 128 μm, the parameter space with better prediction performance of the RC system is greatly improved. When performing the Santa-Fe chaotic time series prediction task, the normalized mean square error (<i>NMSE</i>) is less than 0.01, and the parameter range of the injection strength is increased by about 22%. The range of parameter with <i>NMSE</i> no more than 0.1 is improved by nearly 40% for 10<sup>th</sup> order Nonlinear Auto-Regressive Moving Average (NARMA-10) task. When the number of virtual nodes is 50, the system can achieve high-precision prediction for the above two tasks. This is of great significance for the practical development of the system.

Study on Connectivity Analysis and Injection–Production Optimization of Strong Heterogeneous Sandstone Reservoir Based on Connectivity Method

“…In Formula ( 6): q n i,j is the flow rate from well j to well i; m 3 /s. Considering the influence of edge and bottom water in the reservoir, it is deduced according to the theory of front edge propulsion [26][27][28]. By setting an upstream node position x u of x, the saturation of the corresponding node position is S wu , and it is concluded that:…”