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首页> 外文期刊>Soft Computing >Multilayer Feedforward Neural Network Based on Multi-valued Neurons (MLMVN) and a Backpropagation Learning Algorithm
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Multilayer Feedforward Neural Network Based on Multi-valued Neurons (MLMVN) and a Backpropagation Learning Algorithm

机译：基于多值神经元（MLMVN）和反向传播学习算法的多层前馈神经网络

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A multilayer neural network based on multi-valued neurons (MLMVN) is considered in the paper. A multi-valued neuron (MVN) is based on the principles of multiple-valued threshold logic over the field of the complex numbers. The most important properties of MVN are: the complex-valued weights, inputs and output coded by the kth roots of unity and the activation function, which maps the complex plane into the unit circle. MVN learning is reduced to the movement along the unit circle, it is based on a simple linear error correction rule and it does not require a derivative. It is shown that using a traditional architecture of multilayer feedforward neural network (MLF) and the high functionality of the MVN, it is possible to obtain a new powerful neural network. Its training does not require a derivative of the activation function and its functionality is higher than the functionality of MLF containing the same number of layers and neurons. These advantages of MLMVN are confirmed by testing using parity n, two spirals and “sonar” benchmarks and the Mackey–Glass time series prediction.
机译：本文考虑了基于多值神经元（MLMVN）的多层神经网络。多值神经元（MVN）基于复数域上的多值阈值逻辑原理。 MVN的最重要属性是：复数值权重，由单位的第k个根和激活函数（将复平面映射到单位圆）编码的输入和输出。 MVN学习被简化为沿单位圆的运动，它基于简单的线性误差校正规则，并且不需要导数。结果表明，使用多层前馈神经网络（MLF）的传统体系结构和MVN的高功能性，有可能获得一种新的强大的神经网络。它的训练不需要激活函数的派生，并且其功能性高于包含相同数量的层和神经元的MLF的功能性。 MLMVN的这些优势已通过使用奇偶校验n，两个螺旋线和“声纳”基准以及Mackey-Glass时间序列预测进行测试得到了证实。

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