英和和英

出典:『Wikipedia』 (2010/07/26 01:33 UTC 版)

英語による解説

引用

Reservoir computing is a framework for computation like a neural network. Typically an input signal is fed into a fixed (random) dynamical system called reservoir and the dynamics of the reservoir map the input to a higher dimension. Then a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output. The main benefit is that the training is performed only at the readout stage and the reservoir is fixed. Liquid-state machines and echo state networks are two major types of reservoir computing.