Frequency-based multilayer neural network with on-chip learning and enhanced neuron characteristics
Frequency-based multilayer neural network with on-chip learning and enhanced neuron characteristics
复制标题
具有片上学习和增强神经元特性的基于频率的多层神经网络
DOI：
10.1109/72.761711
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发表时间：
1999
影响因子：
--
通讯作者：
H. Hikawa
中科院分区：
文献类型：
--
作者：
H. Hikawa
提出了一种具有片上学习功能的基于频率的多层神经网络（MNN）的数字结构。由于信号电平以频率表示，因此用简单的变频器代替乘法器，神经元单元采用投票电路作为非线性加法器，改善了非线性特性。此外，采用脉冲乘法器增强神经元的特性。针对片上学习，对反向传播算法进行了改进。提出的MNN结构在现场可编程门阵列（FPGA）上实现，并进行了各种实验来测试系统的性能。实验结果表明，由于投票电路的存在，所提出的神经元具有很好的非线性功能。采用片上学习方法对MNN的学习行为进行了实验测试，结果表明该MNN具有良好的学习能力和泛化能力。本文提出的MNN结构简单、模块化，具有大规模并行和灵活的网络架构，非常适合于超大规模集成（VLSI）的实现。
A new digital architecture of the frequency-based multilayer neural network (MNN) with on-chip learning is proposed. As the signal level is expressed by the frequency, the multiplier is replaced by a simple frequency converter, and the neuron unit uses the voting circuit as the nonlinear adder to improve the nonlinear characteristic. In addition, the pulse multiplier is employed to enhance the neuron characteristics. The backpropagation algorithm is modified for the on-chip learning. The proposed MNN architecture is implemented on field programmable gate arrays (FPGA's) and the various experiments are conducted to test the performance of the system. The experimental results show that the proposed neuron has a very good nonlinear function owing to the voting circuit. The learning behavior of the MNN with on-chip learning is also tested by experiments, which show that the proposed MNN has good learning and generalization capabilities. Simple and modular structure of the proposed MNN leads to a massive parallel and flexible network architecture, which is well suited for very large scale integration (VLSI) implementation.