Abstract
Bp learning has been used very often as methods for neural network's learning. If the network has enough neurons to classify the training data, BP learning is well known to perform good classification. However, the network is so called "Black Box", because it is difficult to give clear explanation on the relation between inputs and outputs. Recently, some methods for extracting some rules from the regularity, which appears on hidden neurons, have been proposed. These methods cannot make clear the relation of extracted rules. In this paper, we propose a learning method of neural network with lattice architecture. This network structure is consisted of hidden neurons in the lattice. To obtain the optimal network structure, we apply a neuron generation/annihilation algorithm without a dependency on an initial network structure. To verify the validity and effectiveness of proposed method, we have experimented to the function identification and classification of any points in a cube to compare with BP learning and RBF network.
