A new neural network structure: node-to-node-link neural network

Scientific Research Publishing, Inc.
Publication Type:
Journal Article
Journal of Intelligence Learning Systems and Application, 2011, 2 (1), pp. 1 - 11
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010000083.pdf330.55 kB
Adobe PDF
This paper presents a new neural network structure and namely node-to-node-link neural network (N-N-LNN) and it is trained by real-coded genetic algorithm (RCGA) with average-bound crossover and wavelet mutation [1]. The N-N-LNN exhibits a node-to-node relationship in the hidden layer and the network parameters are variable. These characteristics make the network adaptive to the changes of the input environment, enabling it to tackle different input sets distributed in a large domain. Each input data set is effectively handled by a corresponding set of network parame-ters. The set of parameters is governed by other nodes. Thanks to these features, the proposed network exhibits better learning and generalization abilities. Industrial application of the proposed network to hand-written graffiti recognition will be presented to illustrate the merits of the network.
Please use this identifier to cite or link to this item: