Lazy Quantum clustering induced radial basis function networks (LQC-RBFN) with effective centers selection and radii determination

Cui Yiqian; Shi Junyou; Wang Zili

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Lazy Quantum clustering induced radial basis function networks (LQC-RBFN) with effective centers selection and radii determination

机译：具有有效中心选择和半径确定的惰性量子簇诱导径向基函数网络（LQC-RBFN）

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The Radial Basis Function Networks (RBFN) model has been successfully applied to different application scenarios as a universal approximator because of its simple architecture and online training capability. The approximation capability of RRBFN is greatly dependent on determination of the centers and the radii of the radial basis functions (RBFs) in the networks structure. Statistics-based centers determination approaches like K-means fail to capture and preserve the training data structure. In this paper, a new unsupervised RBFN construction methodology called Lazy Quantum Clustering induced Radial Basis Function Networks (LQC-RBFN) is proposed. It inherits the advantage of data structure learning and shows high robustness towards data distribution of Quantum Clustering (QC). At the same time, the controlling parameter can be determined arbitrarily without the requirement of precise calibration, and the minima search is done only once for a specific training data set. The centers and radii are selected based on the potential function generated by quantum assimilation, and the networks structure is adaptively updated incorporating the centers information. A series of application studies are presented to verify the effectiveness of the proposed LQC-RBFN model. (C) 2015 Elsevier B.V. All rights reserved.

机译：径向基函数网络（RBFN）模型由于其简单的体系结构和在线训练功能而已成功地作为通用逼近器应用于不同的应用场景。 RRBFN的逼近能力在很大程度上取决于网络结构中中心的确定以及径向基函数（RBF）的半径。基于统计的中心确定方法（例如K均值）无法捕获和保留训练数据结构。本文提出了一种新的无监督RBFN构造方法，称为惰性量子聚类诱导径向基函数网络（LQC-RBFN）。它继承了数据结构学习的优势，并显示了对量子聚类（QC）数据分布的高度鲁棒性。同时，无需精确校准即可任意确定控制参数，并且对于特定训练数据集只需进行一次最小搜索。基于量子同化产生的势函数来选择中心和半径，并结合中心信息来自适应地更新网络结构。提出了一系列应用研究，以验证所提出的LQC-RBFN模型的有效性。（C）2015 Elsevier B.V.保留所有权利。

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来源
《Neurocomputing》 |2016年第29期|797-807|共11页
作者
Cui Yiqian; Shi Junyou; Wang Zili;
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作者单位

Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China|Beihang Univ, Sci & Technol Key Lab Reliabil & Environm Engn, Beijing 100191, Peoples R China;

Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China|Beihang Univ, Sci & Technol Key Lab Reliabil & Environm Engn, Beijing 100191, Peoples R China;

Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China|Beihang Univ, Sci & Technol Key Lab Reliabil & Environm Engn, Beijing 100191, Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Radial basis function networks (RBFN); Lazy Quantum Clustering (LQC); Potential function; Networks structure adaptation; Unsupervised networks model;

机译：径向基函数网络（RBFN）;惰性量子聚类（LQC）;势函数;网络结构自适应;无监督网络模型;

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1. A Neural Network Ensemble Classifier for Effective Intrusion Detection Using Fuzzy Clustering and Radial Basis Function Networks [J] . Amini Mohammad, Rezaeenour Jalal, Hadavandi Esmaeil International Journal of Artificial Intelligence Tools: Architectures, Languages, Algorithms . 2016,第2期

机译：使用模糊聚类和径向基函数网络进行有效入侵检测的神经网络集成分类器
2. LOGICAL RADIAL BASIS FUNCTION NETWORKS: A HYBRID MODEL FOR EFFICIENT IMPLEMENTATION OF CLASSICAL RADIAL BASIS FUNCTION NETWORKS [J] . H.A. Hefny, A.A. Bahnasawi, A.H. Abdel Wahab, Control and Intelligent Systems . 2002,第3期

机译：逻辑径向基函数网络：有效实现经典径向基函数网络的混合模型
3. RADIAL BASIS FUNCTION NEURAL NETWORKS FOR TRANSIENT STABILITY ASSESSMENT PART I: THEORY OF RADIAL BASIS FUNCTION NEURAL NETWORKS [J] . S. Muknahallipatna, B.H. Chowdhury International journal of power & energy systems . 2001,第2期

机译：暂态稳定评估的径向基神经网络，第一部分：径向基神经网络的理论
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机译：通过自适应范围遗传算法使用径向基函数网络的最佳设计（径向基函数网络中半径的确定）
5. On-line radial basis function network center adaptation for nonlinear adaptive identification and control. [D] . Chan, Alistair Keating. 1995

机译：在线径向基函数网络中心自适应，用于非线性自适应识别和控制。
6. Separation and Determination of Honokiol and Magnolol in Chinese Traditional Medicines by Capillary Electrophoresis with the Application of Response Surface Methodology and Radial Basis Function Neural Network [O] . Ping Han, Feng Luan, Xizu Yan, -1

机译：响应面分析-径向基函数神经网络-毛细管电泳法分离测定中药中厚朴酚和厚朴酚
7. A better selection of patterns in lazy learning radial basis neural networks [O] . Isasi, Pedro, Valls, José M., Galván, Inés M. 2003

机译：在惰性学习径向基神经网络中更好地选择模式
8. Clustering-Based Algorithms for Radial Basis Function and Sigmoid Perceptron Networks. [R] . Ulykan, Z. 2001

机译：基于聚类的径向基函数和sigmoid感知器网络算法。

Lazy Quantum clustering induced radial basis function networks (LQC-RBFN) with effective centers selection and radii determination

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