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Learning Capability of T-Model Neural Network
Okihiko ISHIZUKA Zheng TANG Tetsuya INOUE Hiroki MATSUMOTO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1992/07/25
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
neural network, fully interconnection, half interconnection, feedforward network,
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We introduce a novel neural network called the T-Model and investigates the learning ability of the T-Model neural network. A learning algorithm based on the least mean square (LMS) algorithm is used to train the T-Model and produces a very good result for the T-Model network. We present simulation results on several practical problems to illustrate the efficiency of the learning techniques. As a result, the T-Model network learns successfully, but the Hopfield model fails to and the T-Model learns much more effectively and more quickly than a multi-layer network.