Modeling Walking Behavior of Powered Exoskeleton Based on Complex-Valued Neural Network

2018 ◽  
Author(s):  
Yudai Ishizuka ◽  
Shota Murai ◽  
Yasutake Takahashi ◽  
Masayuki Kawai ◽  
Yoshiaki Taniai ◽  
...  
Keyword(s):  
Neural Network ◽  
Walking Behavior ◽  
Powered Exoskeleton ◽  
Complex Valued

scholarly journals An Approach for Sound Source Localization by Complex-Valued Neural Network

2013 ◽  
Vol E96.D (10) ◽  
pp. 2257-2265 ◽  
Author(s):  
Hirofumi TSUZUKI ◽  
Mauricio KUGLER ◽  
Susumu KUROYANAGI ◽  
Akira IWATA
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Sound Source Localization ◽  
Complex Valued

scholarly journals A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA

2019 ◽  
Vol 9 (1) ◽  
pp. 5-19 ◽  
Author(s):  
Igor Aizenberg ◽  
Antonio Luchetta ◽  
Stefano Manetti ◽  
Maria Cristina Piccirilli
Keyword(s):  
Neural Network ◽  
Frequency Response ◽  
Learning Algorithm ◽  
Repeated Measurements ◽  
Distributed Parameter ◽  
Response Analysis ◽  
Frequency Response Analysis ◽  
Arbitrary Complex ◽  
Lumped Models ◽  
Complex Valued

Abstract 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.

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