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首页> 外文期刊>Journal of Computer Sciences and Applications >On Comparative Analogy between Ant Colony Systems and Neural Networks Considering Behavioral Learning Performance
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On Comparative Analogy between Ant Colony Systems and Neural Networks Considering Behavioral Learning Performance

机译：考虑行为学习绩效的蚁群系统与神经网络的比较类比

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摘要

This article addresses an interesting comparative analytical study. The presented study considers two concepts of diverse algorithmic biological behavioral learning approach. Those concepts for computational intelligence are tightly related to neural and non-neural Systems. Respectively, the first algorithmic intelligent approach concerned with observed obtained practical results after three neural animal systems’ activities. Namely, they are Pavlov’s, and Thorndike’s experimental work. Furthermore, a mouse’s trials during its movement inside figure of eight (8) maze, those aiming to reach optimal solution for reconstruction problem. However, second algorithmic intelligent approach conversely originated from observed activities’ results for non-neural Ant Colony System (ACS). Those results have been obtained after reaching optimal solution solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance shown to be similar for both introduced neural and non-neural systems. Considering observed two systems' performances, it has shown both to be in agreement with learning convergence process searching for Least Mean Square (LMS) error algorithm. Accordingly, adopted ANN modeling is realistically relevant tool systematic observations' investigation and performance analysis for both selected computational intelligence (biological behavioral learning) systems.
机译：本文介绍了一个有趣的比较分析研究。本研究考虑了不同算法生物学行为学习方法的两个概念。这些用于计算智能的概念与神经系统和非神经系统紧密相关。在三个神经动物系统的活动之后，分别涉及观察到的第一个算法智能方法分别获得了实际结果。也就是说，它们是巴甫洛夫（Pavlov）和桑代克（Thorndike）的实验性作品。此外，老鼠在八（8）个迷宫中移动时进行了试验，这些试验旨在为重建问题提供最佳解决方案。然而，第二种算法智能方法则相反地源自对非神经蚁群系统（ACS）观察到的活动结果。在达到最佳解决旅行商问题（TSP）的解决方案之后，即可获得这些结果。有趣的是，对于引入的神经系统和非神经系统，增加试剂（神经元或蚂蚁）数量对学习性能的影响显示出相似的效果。考虑到观察到的两个系统的性能，已表明两者都与搜索最小均方误差算法的学习收敛过程一致。因此，对于两个选定的计算智能（生物行为学习）系统，采用的ANN建模都是与实际相关的工具系统的观察调查和性能分析。

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