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S. H. Ling, F. H. F. Leung, H. K. Lam, and P. K. S. Tam, “Short-term electric load forecasting based on a neural fuzzy network,” IEEE Transactions on Industrial Electronics, Vol. 50, No. 6, pp. 1305–1316, December 2003.

has been cited by the following article:

• TITLE: A New Neural Network Structure: Node-to-Node-Link Neural Network

  AUTHORS: S. H. Ling

  KEYWORDS: Genetic Algorithm, Hand-Written Graffiti Recognition, Neural Network

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.2 No.1, March 1, 2010

  ABSTRACT: 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.