Geomatics Engineering, Engineering Faculty, Erciyes University, Turkey E-Mail:
Sensors (Basel). 2009;9(8):6312-29. doi: 10.3390/s90806312. Epub 2009 Aug 12.
This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others.
本文介绍了用于分类目的的径向基函数 (RBF) 神经网络的训练算法比较。RBF 网络在许多科学和工程领域提供了有效的解决方案。它们在模式分类和信号处理领域特别受欢迎。已经提出了几种用于训练 RBF 网络的算法。人工蜂群 (ABC) 算法是一种新的、非常简单和强大的基于群体的优化算法，它受到了蜜蜂群体智能行为的启发。ABC 算法的训练性能与遗传算法、卡尔曼滤波算法和梯度下降算法进行了比较。在实验中，不仅使用了 UCI 存储库中众所周知的分类问题，如鸢尾花、葡萄酒和玻璃数据集，还设计了一个实验设置，并实现了用于自主地面车辆的基于惯性传感器的地形分类。实验结果表明，与其他算法相比，使用 ABC 算法可以获得更好的学习效果。
