NIC算法与人工神经网络的整合:不同方法的回顾

Ramandeep Kaur, Amar Singh, Jimmy Singla
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引用次数: 3

摘要

自然启发算法的灵感来自于我们的环境。这些算法的灵感来自昆虫、鸟类和动物等的行为。同样,人工神经网络(ANN)是人脑的复制。这两种算法都用于解决现实世界中的复杂和非线性问题。在本文中,我们提出了不同的基于自然启发计算(NIC)的技术,用于设计和发展人工神经网络。研究结果表明,人工设计人工神经网络是非常困难的,因此需要对人工神经网络进行自动设计。反向传播(BP)方法可以与任何自然启发的算法(如PSO, ABC或ACO)结合使用,因为单独使用它们不能给出准确的结果。与单独使用两者相比,混合算法显示出更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of NIC algorithms and ANN: A review of different approaches
Nature inspired algorithms are inspired from our environment. These algorithms are inspired by the behaviour of insects, birds and animals etc. In the same way, the artificial neural network (ANN) is the replication of a human brain. Both types of algorithms are used to solve the complex and nonlinear problems of real world. In this paper, we have presented different nature inspired computing (NIC) based techniques, which are used to design and evolve ANN. The results of our study have shown that the manual designing of ANN is very difficult, so we need to design ANN automatically. The Back propagation (BP) method can be used with the combination of any nature inspired algorithm like PSO, ABC or ACO, as individually, they are not giving accurate results. The hybrid algorithms have demonstrated better results as compared to the individual uses of both.
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