神经网络和非线性控制系统的准周期AlGaAs超晶格
Quasiperiodic AlGaAs superlattices for neuromorphic networks and nonlinear control systems
摘要：
提出了准周期AlGaAs超晶格作为FitzHugh-Nagumo神经形态网络的非线性元件的应用，并以Fibonacci和figurate超晶格为例进行了理论研究。通过对Fibonacci超晶格的符号序列进行分解，产生了图形超晶格的符号序列。分解的每一段的长度等于相应的图数。它示出了基于斐波那契和figurate超晶格的非线性网络提供了更好的半色调图片的并行过滤比基于传统的二极管，具有立方电压-电流特性的网络。结果发现，作为非线性网络的元件的图形超晶格F011（1）提供的滤波误差几乎是传统的“立方”二极管的两倍。这些优点是由准周期超晶格的电压-电流特性的减少部分的波状形状来解释的，这导致了网络单元的多稳定性。这种多稳定性预示着新的有趣的非线性动力学现象。电压-电流特性的各种波动形式为准周期超晶格，特别是定形超晶格在许多领域--从神经系统建模到非线性控制系统开发--开辟了新的有趣的可能性
英文摘要：
The application of quasiperiodic AlGaAs superlattices as a nonlinear element of the FitzHugh-Nagumo neuromorphic network is proposed and theoretically investigated on the example of Fibonacci and figurate superlattices. The sequences of symbols for the figurate superlattices were produced by decomposition of the Fibonacci superlattices' symbolic sequences. A length of each segment of the decomposition was equal to the corresponding figurate number. It is shown that a nonlinear network based upon Fibonacci and figurate superlattices provides better parallel filtration of a half-tone picture than a network based upon traditional diodes which have cubic voltage-current characteristics. It was found that the figurate superlattice F011(1) as a nonlinear network's element provides the filtration error almost twice less than the conventional "cubic" diode. These advantages are explained by a wavelike shape of the decreasing part of the quasiperiodic superlattice's voltage-current characteristic, which leads to multistability of the network's cell. This multistability promises new interesting nonlinear dynamical phenomena. A variety of wavy forms of voltage-current characteristics opens up new interesting possibilities for quasiperiodic superlattices and especially for figurate superlattices in many areas - from nervous system modeling to nonlinear control systems development
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