Reservoir computing approaches to recurrent neural network training
Reservoir computing approaches to recurrent neural network training
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DOI：
10.1016/j.cosrev.2009.03.005
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发表时间：
2009-08-01
影响因子：
12.9
通讯作者：
Jaeger, Herbert
中科院分区：
文献类型：
--
作者：
Lukosevicius, Mantas;Jaeger, Herbert
回声状态网络和液态机在人工循环神经网络（RNN）训练中引入了一种新的范例，其中随机生成RNN（水库），只训练一个读出。该范式被称为储层计算，极大地促进了rnn的实际应用，并在许多任务中优于经典的完全训练的rnn。近年来，它已成为一个生动的研究领域，对包括水库适应在内的基本思想进行了大量扩展，从而拓宽了使用不同方法训练水库和读出的初始范式。本文系统地综述了目前生成/适应储层和训练不同类型读数的方法。它提供了技术的自然概念分类，超越了当前油藏方法的“品牌”界限，因此旨在帮助统一该领域，并为读者提供详细的“地图”。(C) 2009爱思唯尔公司版权所有。
Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir computing, greatly facilitated the practical application of RNNs and outperformed classical fully trained RNNs in many tasks. It has lately become a vivid research field with numerous extensions of the basic idea, including reservoir adaptation, thus broadening the initial paradigm to using different methods for training the reservoir and the readout. This review systematically surveys both current ways of generating/adapting the reservoirs and training different types of readouts. It offers a natural conceptual classification of the techniques, which transcends boundaries of the current ''brand-names'' of reservoir methods, and thus aims to help in unifying the field and providing the reader with a detailed ''map'' of it. (C) 2009 Elsevier Inc. All rights reserved.