A Feedforward Neural Network based on Multi-Valued Neurons

A feedforward neural network based on multi-valued neurons is considered in the paper. It is shown that using a traditional feedforward architecture and a high functionality multi-valued neuron, it is possible to obtain a new powerful neural network. Its learning does not require a derivative of the activation function and its functionality is higher than the functionality of traditional feedforward networks 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.

• Type: Conference
• Year: 2004
• Where: FUZZY
• Authors: Igor N. Aizenberg, Claudio Moraga, Dmitriy Paliy
• Keywords: Artificial Intelligence, Feedforward Neural Network, FUZZY 2004, Multi-valued Neuron, Traditional Feedforward