Backpropagation algorithm for logic oriented neural networks
Takeshi Kamio, Shinichi Tanaka, Michitada Morisue · 2000
Multilayer feedforward neural network (MFNN) trained by the backpropagation (BP) algorithm is one of the most significant models in artificial neural networks. Although they have been implemented as analog, mixed analog-digital and fully digital VLSI circuits, it is still difficult to realize their hardware implementation with BP learning function. This paper describes the BP algorithm for the logic oriented neural network (LOGO-NN) which we have proposed as a kind of MFNN with quantized weights and multilevel threshold neurons. Since both weights and neuron outputs are quantized to integer values in LOGO-NNs, it is expected that LOGO-NNs with BP learning can be more effectively implemented than the common MFNNs. Finally, it is shown by simulations that the proposed BP algorithm has good performance for LOGO-NNs.