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Fully Complex-valued Dendritic Neuron Model
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DOI：10.1109/TNNLS.2021.3105901.png)
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Abstract
En 中文
A single dendritic neuron model (DNM) that owns the nonlinear information processing ability of dendrites has been widely used for classification and prediction. Complex-valued neural networks that consist of a number of multiple/deep-layer McCulloch-Pitts neurons have achieved great successes so far since neural computing was utilized for signal processing. Yet no complex value representations appear in single neuron architectures. In this article, we first extend DNM from a real-value domain to a complex-valued one. Performance of complex-valued DNM (CDNM) is evaluated through a complex xor problem, a non-minimum phase equalization problem, and a real-world wind prediction task. Also, a comparative analysis on a set of elementary transcendental functions as an activation function is implemented and preparatory experiments are carried out for determining hyperparameters. The experimental results indicate that the proposed CDNM significantly outperforms real-valued DNM, complex-valued multi-layer perceptron, and other complex-valued neuron models.
Keywords:
Neurons
Biological neural networks
Task analysis
Convergence
Dendrites (neurons)
Computer architecture
Computational modeling
Activation functions
complex back-propagation (BP)
complex domain
complex-valued neural networks
dendritic neuron model (DNM)
elementary transcendental functions
McCulloch-Pitts neuron
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8.9
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7.6K
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