20
个编辑
更改
无编辑摘要
'''Reservoir computing''' is a framework for computation derived from [[recurrent neural network]] theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir.<ref name=":4">{{Cite journal|last1=Tanaka|first1=Gouhei|last2=Yamane|first2=Toshiyuki|last3=Héroux|first3=Jean Benoit|last4=Nakane|first4=Ryosho|last5=Kanazawa|first5=Naoki|last6=Takeda|first6=Seiji|last7=Numata|first7=Hidetoshi|last8=Nakano|first8=Daiju|last9=Hirose|first9=Akira|title=Recent advances in physical reservoir computing: A review|journal=Neural Networks|volume=115|pages=100–123|doi=10.1016/j.neunet.2019.03.005|pmid=30981085|issn=0893-6080|year=2019|doi-access=free}}</ref> After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output.<ref name=":4" /> The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed.<ref name=":4" /> The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be used to reduce the effective computational cost.<ref>{{Cite journal|last1=Röhm|first1=André|last2=Lüdge|first2=Kathy|date=2018-08-03|title=Multiplexed networks: reservoir computing with virtual and real nodes|journal=Journal of Physics Communications|volume=2|issue=8|pages=085007|bibcode=2018JPhCo...2h5007R|doi=10.1088/2399-6528/aad56d|issn=2399-6528|doi-access=free}}</ref>
'''Reservoir computing''' is a framework for computation derived from [[recurrent neural network]] theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir.<ref name=":4">{{Cite journal|last1=Tanaka|first1=Gouhei|last2=Yamane|first2=Toshiyuki|last3=Héroux|first3=Jean Benoit|last4=Nakane|first4=Ryosho|last5=Kanazawa|first5=Naoki|last6=Takeda|first6=Seiji|last7=Numata|first7=Hidetoshi|last8=Nakano|first8=Daiju|last9=Hirose|first9=Akira|title=Recent advances in physical reservoir computing: A review|journal=Neural Networks|volume=115|pages=100–123|doi=10.1016/j.neunet.2019.03.005|pmid=30981085|issn=0893-6080|year=2019|doi-access=free}}</ref> After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output.<ref name=":4" /> The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed.<ref name=":4" /> The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be used to reduce the effective computational cost.<ref>{{Cite journal|last1=Röhm|first1=André|last2=Lüdge|first2=Kathy|date=2018-08-03|title=Multiplexed networks: reservoir computing with virtual and real nodes|journal=Journal of Physics Communications|volume=2|issue=8|pages=085007|bibcode=2018JPhCo...2h5007R|doi=10.1088/2399-6528/aad56d|issn=2399-6528|doi-access=free}}</ref>
储备池计算是一个从循环神经网络理论中得出来的计算框架，储备池是一个固定的，非线性系统，其内部具有动力学过程，这个动力学过程将输入信号映射到更高维的计算空间。当输入信号被送入储备池(储备池通常被当作一个“黑匣子”)后，可以训练一个简单的读出机制来读取储备池中神经元的状态并将其映射到所需的输出。这个框架的第一个关键好处是，训练只在读出阶段进行，在读出阶段储备池动力学特性保持不变。第二个好处是这个储备池系统的计算能力，无论是在经典力学还是量子力学中，都可以有效的降低计算成本。
== History ==
== History ==