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## Abstract Various approaches have been proposed to construct reservoir computing systems. However, the network structure and information processing capacity of these systems are often tied to their individual implementations, which typically become difficult to modify after physical setup. This limitation can hinder performance when the system is required to handle a wide spectrum of prediction tasks. To address this limitation, it is crucial to develop tunable systems that can adapt to a wide range of problem domains. This chapter presents a tunable optical computing method based on the iterative function system (IFS). The tuning capability of IFS provides adjustment of the network structure and optimizes the performance of the optical system. Numerical and experimental results show the tuning capability of the IFS reservoir computing. The relationship between tuning parameters and reservoir properties is discussed. We further investigate the impact of optical feedback on the reservoir properties and present the prediction results.

Author

Suguru Shimomura

Publisher

Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd

Published

2023

Language

EN

ISBN

9789819950713

Subjects

Science, Computer Science, Mathematics