Approximate-Linear Neuron and Non-Linear Neuron

Q. Liu, Z.D. Zhou, X.M. Jiang, and T.J. Huang (PRC)


approximatelinear neuron (ALN),nonlinear neuron (NLN), Hopfield neural network, lifegame


The neuron model normally widely applied in artificial neural network (ANN) has the property of monotone and bounded I/O. This paper names it as approximate-linear neuron (ALN). It simplifies the complexity of the neural network: at the same time, it becomes a bottleneck that limits the network’s abilities. In contrast, by comparing the stability of Hopfield neural network with the complex behavior of Cconway’s life game, this paper prefers a new concept, non-linear neuron (NLN), of which I/O property is non-linear completely. NLN can further enhance the non-linear properties of ANN, so an ANN with NLN will be more powerful. Remainder ANN is an example.

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