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7-Instant Discrete-Time Synthesis Model Solving Future Different-Level Linear Matrix System via Equivalency of
This study introduces a novel 7-instant discrete-time synthesis (DTS) model for solving complex future different-level linear matrix systems. The new model, based on zeroing neural network (ZNN) equivalency, demonstrates superior performance in future computation tasks.
Area of Science:
- Numerical Analysis
- Computational Mathematics
- Artificial Intelligence
Background:
- Traditional methods struggle with complex, future-computation-dependent linear matrix systems.
- Future different-level linear matrix systems present unique challenges due to their structure and forward-looking computation needs.
Purpose of the Study:
- To develop an effective method for solving future different-level linear matrix systems.
- To introduce the concept of Zeroing Neural Network (ZNN) equivalency (ZE) for these systems.
- To propose a high-precision discrete-time synthesis (DTS) model for future computation.
Main Methods:
- Consideration of a continuous different-level linear matrix system.
- Development of the Zeroing Neural Network (ZNN) equivalency (ZE).
- Formulation of a continuous-time synthesis (CTS) model based on ZE.
- Derivation of a 7-instant discrete-time synthesis (DTS) model using a high-precision 7-instant Zhang et al. discretization (ZeaD) formula.
Main Results:
- The proposed 7-instant DTS model effectively addresses the future-computation requirement.
- Theoretical analyses confirm the efficacy of the 7-instant DTS model.
- Experimental results validate the superior performance of the 7-instant DTS model compared to conventional methods.
Conclusions:
- The novel 7-instant DTS model offers a powerful solution for future different-level linear matrix systems.
- ZNN equivalency provides a new theoretical basis for solving these complex systems.
- The developed model demonstrates excellent performance and potential for future computational applications.
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