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2 Journal of Artificial Intelligence and Soft Computing Research

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A MLMVN with arbitrary complex-valued inputs and a hybrid testability approach for the extraction of lumped models using FRA

Aizenberg Igor, Luchetta Antonio, Manetti Stefano, Piccirilli Maria Cristina

Journal of Artificial Intelligence and Soft Computing Research

2019

Vol. 9, No. 1

5--19

A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.

Complex-valued associative memories with projection and iterative learning rules

Isokawa T., Yamamoto H., Nishimura H., Yumoto T., Kamiura N., Matsui N.

Journal of Artificial Intelligence and Soft Computing Research

2018

Vol. 8, No. 3

237--249

In this paper, we investigate the stability of patterns embedded as the associative memory distributed on the complex-valued Hopfield neural network, in which the neuron states are encoded by the phase values on a unit circle of complex plane. As learning schemes for embedding patterns onto the network, projection rule and iterative learning rule are formally expanded to the complex-valued case. The retrieval of patterns embedded by iterative learning rule is demonstrated and the stability for embedded patterns is quantitatively investigated.