Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
A Neural Network Diagnosis Approach for Analog Circuits
Applied Intelligence
Soft Computing - A Fusion of Foundations, Methodologies and Applications
AC modeling of the MOSFET channel series resistance
Analog Integrated Circuits and Signal Processing
Blur Identification by Multilayer Neural Network Based on Multivalued Neurons
IEEE Transactions on Neural Networks
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A novel identification technique for lumped models of general distributed circuits (i.e. microwave transmission lines, monolithic integrated circuits and filters) is presented. The approach is based on a hybrid multi-valued neuron neural network with a modified layer and learning process, whose convergence allows the validation of the approximated lumped model. The modified layer is generated by symbolic analysis of the model under exam. The inputs of the neural network are geometrical parameters, while the outputs represent the estimation of the lumped circuit parameters.