Abstract

Radial Basis Function Neural Networks (RBFNNs) have been widely used for classification and function approximation tasks. So, it is worthy to try improving and developing new learning algorithms for RBFNNs in order to get better results. This paper presents a new learning method for RBFNNs. Hence, an improved learning algorithm for center adjustment of RBFNNs using Harmony search (HS) algorithm has been proposed. The proposed RBFNN is used for diabetes recognition task. In order to increase the recognition accuracy as well as to reduce the dimensionality of feature vectors, Rough Set Theory (RST) has been applied on Pima Indians Diabetes. Comprehensive experiments have been conducted on Proben1 dataset in order to evaluate the efficiency and accuracy of the proposed RBFNN. The experimental results show that the proposed method can achieve higher performance compared to other state-of-the-art in the field.

中文翻译：

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基于和声搜索的糖尿病诊断新径向基函数神经网络设计

摘要

径向基函数神经网络（RBFNN）已广泛用于分类和函数逼近任务。因此，值得尝试改进和开发用于RBFNN的新学习算法，以获得更好的结果。本文提出了一种新的RBFNN学习方法。因此，提出了一种改进的基于谐波搜索（HS）算法的RBFNN中心调整学习算法。提出的RBFNN用于糖尿病识别任务。为了提高识别精度并减少特征向量的维数，粗糙集理论（RST）已应用于皮马印第安人糖尿病。为了评估所提出的RBFNN的效率和准确性，已经对Proben1数据集进行了综合实验。