Autors: Palmer-Brown,D., Kang,M., Lee,S.W.
Title: Meta-Adaptation: Neurons that Change their Mode
Keywords: Snap-Drift Neural Network, SDNN, Adaptive Function Neural Ne

Abstract: This paper will explore the integration of learning modes into a single neural network structure in which layers of neurons and even individual neurons adopt different modes.There are several reasons to explore modal learning in neural networks.One motivation is to overcome the inherent limitations of any given mode;another is inspiration from neuroscience,cognitive science and human learning, where it is impossible to build a serious model without consideration of multiple modes;and a third reason is non-stationary input data, or timevariant learning objectives,where the required mode is a function of time.We will be presented:The Snap-Drift Neural Networkwhich toggles its learning between two modes, either unsupervised or guided by performance feedback; a general approach to swapping between several learning modes in realtime;and an adaptive function neural network,in which adaptation applies simultaneously to both the weights and the individual neuron activation functions.

References

    Issue

    9th WSEAS International Conference on NEURAL NETWORKS (NN’08), pp. 155-166, 2008, Bulgaria,

    Вид: публикация в национален форум