Closed-form expressions of some stochastic adapting equations for nonlinear adaptive activation function neurons.
In recent work, we introduced nonlinear adaptive activation function (FAN) artificial neuron models, which learn their activation functions in an unsupervised way by information-theoretic adapting rules. We also applied networks of these neurons to some blind signal processing problems, such as independent component analysis and blind deconvolution. The aim of this letter is to study some fundamental aspects of FAN units' learning by investigating the properties of the associated learning differential equation systems.
Fiori
Simone
S
Faculty of Engineering, Perugia University, I-05100 Terni, Italy. sfr@unipeg.it
D000200:Action Potentials / Q000502:physiology; D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D019277:Entropy; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013269:Stochastic Processes