基于梯度的差分神经网络的设计和分析,用于解决具有不平等约束的时间变化的二次式问题
Yang Zeng1, Cheng Hua2, Bolin Liao2
1College of Computer Science and Engineering, Jishou University, Jishou, 416000, Hunan, China; College of Communication and Electronic Engineering, Jishou University, Jishou, 416000, Hunan, China.
概括
一个新的渐变差分神经网络 (GDNN) 能够有效地解决不平等受约束的时间变量的二次程序 (IC-TVQP). 与现有方法相比,这种先进的模型表现出卓越的精度和稳定性.
科学领域:
- 优化优化 优化优化
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 不平等受约束的时间变量的二进制程序 (IC-TVQP) 提出了重要的计算挑战.
- 对于动态的,受约束的优化问题,现有的方法往往缺乏效率和准确性.
研究的目的:
- 引入一种新的渐变差分神经网络 (GDNN),以实现有效的IC-TVQP分辨率.
- 提高计算效率,并确保IC-TVQP的有限时间融合.
主要方法:
- 开发了一种GDNN,其中包含了精致的信号-双功率激活功能.
- 针对传统的梯度基神经网络 (CGNN),变量参数收差分神经网络 (VP-CDNN) 和归零神经网络 (ZNN) 进行了比较分析.
- 进行了广泛的数值模拟,以验证性能和稳定性.
主要成果:
- 与CGNN,VP-CDNN和ZNN相比,GDNN实现了卓越的解决方案准确性,其余误差明显低于CGNN,VP-CDNN和ZNN.
- 证明了对缩放因子变化的强化稳定性.
- 成功地将GDNN应用于一个时间变化的金融投资组合优化问题.
结论:
- 拟议的GDNN为IC-TVQP提供了一个高度有效和高效的解决方案.
- 该模型在现实世界的优化场景中表现出实际适用性和稳定性.
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