A generation method for fuzzy rules using neural networks with planar lattice architecture

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