Multilayer neural networks based on multi-valued neurons (MLMVNs)
have been proposed to combine the advantages of complex-valued
neural networks with a plain derivative-free learning algorithm. In
addition, multi-valued neurons (MVNs) offer a multi-valued
threshold logic resulting in the ability to replace multiple
conventional output neurons in classification tasks. Therefore,
several classes can be assigned to one output neuron. This book
introduces a novel approach to assign multiple classes to numerous
MVNs in the output layer. It was found that classes that possess
similarities should be allocated to the same neuron and arranged
adjacent to each other on the unit circle. Since MLMVNs require
input data located on the unit circle, two employed transformations
are reevaluated. The min-max scaler utilizing the exponential
function, and the 2D discrete Fourier transform restricting to the
phase information for image recognition. The evaluation was
performed on the Sensorless Drive Diagnosis dataset and the Fashion
MNIST dataset.
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