Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
An adaptive tracking controller using neural networks for a class of nonlinear systems
IEEE Transactions on Neural Networks
Two regularizers for recursive least squared algorithms in feedforward multilayered neural networks
IEEE Transactions on Neural Networks
A New Adaptive Backpropagation Algorithm Based on Lyapunov Stability Theory for Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Circuits and Systems Part I: Regular Papers
MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition
Applied Computational Intelligence and Soft Computing - Special issue on Applied Neural Intelligence to Modeling, Control, and Management of Human Systems and Environments
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This brief presents a Lyapunov theory-based weight adaptation scheme for a multilayered neural network (MLNN) mainly used to classify a multiple-input-multiple-output (MIMO) problem. Initially, the MLNN system is linearized using Taylor series expansion. Then, the weight adaptation scheme is designed based on the Lyapunov stability theory to iteratively update the weight. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Hence, the Lyapunov theory-based MLNN acts as a MIMO classifier for face recognition. Analysis and discussion on Lyapunov properties of the proposed classifier are included. The performance of the proposed technique is tested on the Olivetti Research Laboratory database for face classification, and some comparisons with existing conventional techniques are given. Simulation results have revealed that our proposed system achieved better performance.