Reservoir Computing (RC) is an Artificial Neural Network (ANNs) that simulates human brain behavior and thinking. As a new type of ANNs, RC does not require training for its reservoir layer, only for the output weights. This simple training method enables RC to be implemented in a physical way. This article successfully builds a time-delayed optical feedback RC system based on semiconductor lasers using the nonlinear characteristics of optics. The system adopts the idea of time division multiplexing, uses the delay loop of optical feedback to construct a reservoir, and sets up a large number of virtual nodes in the feedback loop to replace traditional reservoir nodes. Finally, we successfully implemented spoken digit recognition tasks through this RC system.
Reserve computing, inspired by the brain's information-processing capabilities, is well suited for tasks that deal with time-related data. Recently, optoelectronic reservoir computing using a semiconductor laser with optical feedback and optical injection as single nonlinear node architecture has gained significant attention. By introducing mask preprocessing into the data, machine learning methods that leverage the nonlinear dynamics of semiconductor lasers for information processing have become widely recognized. However, while the reservoir computing structure appears simple, it is challenging to understand the data variations. In this paper, we conduct numerical simulations of the model, simply expound on the data flow within the system, and complete the task of short-term chaotic time-series prediction.
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