Learning intialized by topologically correct representation

In this research, we proposed a model of a hierarchical three-layered perceptron, in which the middle layer contains a two dimensional map where the topological relationship of the high dimensional input data (external world) are internally represented. The proposed model executes a two-phase learning algorithm where the supervised learning of the output layer is proceeded by a self-organization unsupervised learning of the hidden layer. The objective of this study is to build a simple neural network model which is more biologically realistic than the standard Multilayer Perceptron model and that can form an internal representation that supports its learning potential. The characteristics of the proposed model are demonstrated using several benchmark classification problems.