对于非线性拉比诺维奇-法布里坎特模型,使用主动集法进行启发式计算.
Zulqurnain Sabir1,2, Dumitru Baleanu3,4,5,6, Sharifah E Alhazmi7
1Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
Heliyon
|November 30, 2023
概括
使用人工神经网络,遗传算法和主动集合方法 (ANNs-GAAS) 的新随机计算方法可靠地以高精度解决非线性拉比诺维奇-法布里坎特模型.
科学领域:
- 计算物理 计算物理
- 应用数学 应用数学 应用数学
- 人工智能的人工智能
背景情况:
- 拉比诺维奇-法布里坎特模型是一个复杂的非线性系统,涉及三个普通微分方程.
- 解决这些非线性模型往往需要强大而准确的数值方法.
研究的目的:
- 引入和验证一种新的启发式方法来解决非线性拉比诺维奇-法布里坎特模型.
- 证明拟议的计算方法的可靠性和准确性.
主要方法:
- 该研究采用一种称为ANNs-GAAS的混合计算技术,将人工神经网络 (ANN) 与全球启发性遗传算法 (GA) 和本地搜索主动集 (AS) 方法相结合.
- 根据差异拉比诺维奇-法布里坎特模型构建一个优点函数.
- 该ANNs-GAAS方法利用一个神经网络结构与十个神经元和一个日志-西格移动函数.
主要成果:
- ANNs-GAAS方法为拉比诺维奇-法布里坎特模型提供了简单,可靠和准确的解决方案.
- 使用GAAS方法优化功效函数可以获得高精度的结果.
- 取得的绝对误差在10-07到10-08之间.
- 与传统解决方案的比较证实了该方法的正确性.
结论:
- 拟议的ANNs-GAAS方法是解决非线性拉比诺维奇-法布里坎特模型的验证和有效技术.
- 该方法的可靠性通过各种统计分析进一步得到证实.
- 这项工作为非线性动力学研究提供了一个强大的计算工具.
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