Abstract:

Engineering neurocomputing, as an effective approach to boost intelligent computing technology, focus a puzzle: the “black box” property of neural network. It means that knowledge learning from neural network implicate the vast connected weights. User can’t understand what the neural network learn and what task the neural network can deal with. And what’s more, user can’t know how the neural network predicts and why the neural network reasons these or those conclusions. In order to solve effectively this puzzle, componentassembled neural network (CAN²) is proposed. Based CAN² technology, We construct comprehensible and reused digital logic neurocomponent library(DLNL). Complex digital logic function is implemented and random classification problems is solved by applying DLNL. Experiment indicates that CAN² can reduce the “black box” property of neural network effectively and has powerful reusability. It is an effective attempt in engineering neurocomputing, can improve user confidence for constructing intelligent system by applying neural network.

Key words: neurocomputing, component, neuron, neural network, reusability