Modeling of widely-linear quaternion valued systems using hypercomplex algorithms
The data-driven optimal modeling and identification of widely-linear quaternion-valued synthetic systems is achieved by using a quaternion-valued gradient based algorithms. To account rigorously for the second-order statistics of the quaternion system, the quaternion least mean square (QLMS) and wid...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/69330/1/Modeling%20of%20widely-linear%20quaternion%20valued%20systems%20using%20hypercomplex%20algorithms.pdf http://psasir.upm.edu.my/id/eprint/69330/ |
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| Institution: | Universiti Putra Malaysia |
| Language: | English |
| Summary: | The data-driven optimal modeling and identification of widely-linear quaternion-valued synthetic systems is achieved by using a quaternion-valued gradient based algorithms. To account rigorously for the second-order statistics of the quaternion system, the quaternion least mean square (QLMS) and widely linear quaternion least mean square (WL-QLMS) were selected. The QLMS is shown to successfully model the quaternion-valued systems and the WL-QLMS is able to model both quaternion and widely-linear quaternion valued systems taking into account the full second-order statistics of the system. Analysis has proven that both algorithms are able to adapt to non-stationary nature of the systems. This approach is supported by simulations of various synthetic systems. |
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