Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The self-organizing relationship (SOR) network employing fuzzy inference based heuristic evaluation
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Identification and control of dynamical systems using the self-organizing map
IEEE Transactions on Neural Networks
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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.