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一个增强的神经网络算法,配备了基于准对立和混乱的正弦弦学习策略

Xuan Xiong¹, Shaobo Li², Fengbin Wu²

¹State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.

Entropy (Basel, Switzerland)

|September 28, 2023

概括

本研究介绍了一种改进的神经网络算法 (NNA),使用准对立学习和混乱的正弦-正弦策略来增强全球优化. 这种新的方法有效地避免了局部最佳值,并加速了复杂工程问题的融合.

科学领域:

计算智能是一种计算智能.
优化算法优化算法
人工神经网络的人工神经网络

背景情况:

全球优化问题在工程中至关重要,神经网络算法 (NNA) 被广泛应用.
许多NNA的局部优势较差,在复杂问题上趋同缓慢.
解决这些局限性对于推进工程应用至关重要.

研究的目的:

提出一个改进的神经网络算法 (NNA),增强全球优化能力.
为了加速收,并防止算法陷入局部最佳状态.
验证拟议的算法在基准函数和工程问题上的有效性.

主要方法:

整合基于准对立的学习以改善搜索空间探索和开发.
介绍了一种新的物流混沌正弦-正弦学习策略,以增强逃离局部最佳值的方法.
使用动态调整因子与逐段线性混沌映射来调整探索空间并改善收.

主要成果:

改进后的NNA在避免局部最佳情况和实现更快的融合方面表现出卓越的表现.
使用CEC 2017功能和工程问题进行的比较分析显示了显著的改进.
统计测试证实了该算法的卓越的全球最佳性和融合速度.

结论:

提议的改进的神经网络算法有效地克服了传统NNA在全球优化方面的局限性.
准对立式学习和混乱的正弦策略的结合为复杂的优化任务提供了强大的解决方案.
该算法显示了在各种工程领域的实用应用的强大潜力,这些领域需要高效的全球优化.

关键词:

2017 年 CEC CEC 2017 年 CEC 2017 年 CEC 一个新的战略战略.混乱的地图绘制.复杂的优化是复杂的优化.神经网络算法的神经网络算法几乎是基于对立的学习.-学习策略的学习策略

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