Author:
Igor Aizenberg
Affiliation:
Texas A&M University-Texarkana, United States
Keyword(s):
Complex-valued neural networks, Derivative-free learning, Pattern recognition, Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex-Valued Neural Networks
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this paper, a new activation function for the multi-valued neuron (MVN) is presented. The MVN is a neuron with complex-valued weights and inputs/output, which are located on the unit circle. Although the MVN has a greater functionality than a sigmoidal or radial basis function neurons, it has a limited capability of learning highly nonlinear functions. A periodic activation function, which is introduced in this paper, makes it possible to learn nonlinearly separable problems and non-threshold multiple-valued functions using a single multi-valued neuron. The MVN’s functionality becomes higher and the MVN becomes more efficient in solving various classification problems. A learning algorithm based on the error-correction rule for an MVN with the introduced activation function is also presented.