An Improved Zhang Neural Network Model Solving the Matrix Inverse Online
| Autor: | Xiaosong Liang, Zongsheng Liu, Jun Zhou, Wudai Liao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Generalized inverse
Artificial neural network Computer science Exponential convergence Inverse Inversion (meteorology) 0102 computer and information sciences 02 engineering and technology 01 natural sciences Error function 010201 computation theory & mathematics 0202 electrical engineering electronic engineering information engineering Applied mathematics 020201 artificial intelligence & image processing Matrix inverse Zhang neural network |
| Zdroj: | 2019 International Conference on Advanced Mechatronic Systems (ICAMechS). |
| DOI: | 10.1109/icamechs.2019.8861662 |
| Popis: | In this paper, an optional Moore-Penrose (M-P) inverse error function is investigated and guarantee the global exponential convergence of the neural model and find out its exact inverse of a given time-invarying matrix. This increases the category of Zhang neural network (ZNN) model based on vector-valued matrix-formed errors function and also broadens the field of network. Choosing appropriate ZNN model based on the optional generalized inverse vector-valued matrix-formed error function, which can be applied to the practical problem of matrix inversion with high complexity, it can replace the traditional numerical algorithm. Compared with the numerical algorithm, the improved ZNN model is more efficient, real-time, and accurate in solving the problem, it satisfies the needs of production and life. In addition, the simulative results validate the theoretical analysis and demonstrate the efficacy of the neural model on static matrix inversion. |
| Databáze: | OpenAIRE |
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