Yohei Saito

Yohei Saito

The squared error normalized by the target output is known as the information processing capacity (IPC) and is used to evaluate the performance of reservoir computing (RC). Since RC aims to learn the relationship between input and output time series, we should evaluate the IPC for infinitely long data rather than the IPC for finite-length data. To evaluate the IPC for infinitely long data using the IPC for finite-length data, we use an asymptotic expansion of the IPC and the least-squares method. Then, we show the validity of our method by numerical simulations.

Atsuki Yokota, Ichiro Kawashima, Yohei Saito et al.

Reservoir Computing (RC) is a bio-inspired machine learning framework, and various models have been proposed. RC is a well-suited model for time series data processing, but there is a trade-off between memory capacity and nonlinearity. In this study, we propose methods to improve the memory capacity of reservoir models by modifying their network configuration except for the inside of reservoirs. The Delay method retains past inputs by adding delay node chains to the input layer with the specified number of delay steps. To suppress the effect of input value increase due to the Delay method, we divide the input weights by the number of added delay steps. The Pass through method feeds input values directly to the output layer. The Clustering method divides the input and reservoir nodes into multiple parts and integrates them at the output layer. We applied these methods to an echo state network (ESN), a typical RC model, and the chaotic Boltzmann machine (CBM)-RC, which can be efficiently implemented in integrated circuits. We evaluated their performance on the NARMA task, and measured information processing capacity (IPC) to evaluate the trade-off between memory capacity and nonlinearity.