Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Fuzzy algorithms for learning vector quantization
IEEE Transactions on Neural Networks
A methodology for constructing fuzzy algorithms for learning vector quantization
IEEE Transactions on Neural Networks
An analysis of the GLVQ algorithm
IEEE Transactions on Neural Networks
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This paper proposes a hybrid neural network model based on the Generalized Learning Vector Quantization(GLVQ) learning algorithm and applies this proposed method to the BIT system of More-Electric Aircraft Electrical Power System (MEAEPS). This paper first discusses the feasibility of application unsupervised neural networks to the BIT system and the representative Generalized LVQ (GLVQ) neural network is selected due to its good performance in clustering analysis. Next, we adopt a new form of loss factor to modify the original GLVQ algorithm in order to make it more suitable for our application. Since unsupervised networks cannot distinguish the similar classes, we add a LVQ layer to the GLVQ network to construct a hybrid neural network model. Finally, the proposed method has been applied to the intelligent BIT system of the MEAEPS, and the results show that the proposed method is promising to improve the performance of the BIT system.