In this paper, the authors first present a method for automated extraction of fuzzy rules using neural networks with a planar lattice architecture. The neural network is composed of three layers-input layer, hidden layer with a lattice architecture and output layer. In the hidden layer, the neurons are arranged in a lattice structure, with each neuron assigned a position in a lattice. Each neuron of the hidden layer is assigned a fuzzy proposition which composes a fuzzy rule. The network is learned structurally with generation/annihilation of neurons. After the rules learning process, one may extract simple fuzzy production rules from the network. Next, the authors extend the method to the cases of multi-dimensional rules. The authors apply the proposed method to generate the diagnostic rules for hernia of an intervertebral disc.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Tazaki et al. (2002) studied this question.