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首页> 外文期刊>IEEE transactions on industrial informatics >Nonconvex and Bound Constraint Zeroing Neural Network for Solving Time-Varying Complex-Valued Quadratic Programming Problem
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Nonconvex and Bound Constraint Zeroing Neural Network for Solving Time-Varying Complex-Valued Quadratic Programming Problem

机译：非耦合约束归零神经网络，用于解决时变复数的二次编程问题

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摘要

Many methods are known to solve the problem of real-valued and static quadratic programming (QP) effectively. However, few of them are still useful to solve the time-varying QP problem in the complex domain. In this study, a nonconvex and bound constraint zeroing neural network (NCZNN) model is designed and theorized to solve the time-varying complex-valued QP with linear equation constraint. Besides, we construct several new types of nonconvex and bound constraint complex-valued activation functions by extending real-valued activation functions to the complex domain. Subsequently, corresponding simulation experiments are conducted, and the simulation results verify the effectiveness and robustness of the proposed NCZNN model. Moreover, the model proposed in this article is further applied to solve the issue of small target detection in remote sensing images, which is modeled to QP problem with linear equation constraint by a serial of conversions based on constrained energy minimization algorithm.
机译：已知许多方法可以有效地解决了实值和静态二次编程（QP）的问题。然而，其中很少有用于解决复杂域中的时变QP问题。在该研究中，设计和理论设计了非凸起和结合约束归零神经网络（NCZNN）模型以解决具有线性方程约束的时变复值QP。此外，我们通过将真实值的激活函数扩展到复杂域来构造几种新类型的非耦合约束复合值激活功能。随后，进行了相应的仿真实验，仿真结果验证了所提出的NCZNN模型的有效性和鲁棒性。此外，本文中提出的模型进一步应用于解决遥感图像中的小目标检测问题，这是通过基于受限能量最小化算法的转化串行转换的线性方程约束的QP问题。

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