Optimum design using radial basis function networks by adaptive range genetic algorithms (determination of radius in radial basis function networks)

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）

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

In this paper we use the radial basis function network (RBF) to approximate the fitness function of genetic algorithms and try to obtain the approximate optimum results. RBF is a kind of neural network that is composed by the number of radial basis function in Gaussian distribution. When the position of basis function and their radius are given, learning system of RRF is summarized in calculation of inverse matrix. Thus, learning system is quite simple and very rapid. There are two important issues in RBF, one is to place basis functions and to give data, and the other is to give appropriate radius to each radial basis function. As for the first one, we have proposed the data distribution and basis function distribution method together with Adaptive Range Genetic Algorithms (ARange GAs). In this study, we will focus our attention to the second problem. For that purpose, we give oval distribution for basis function and assume that every basis function have the same oval radius. In this way we can reduce the number' of radius function as the number of design variables. We will show the effectiveness of the proposed method through benchmark problem.

机译：在本文中，我们使用径向基函数网络（RBF）来近似遗传算法的健身功能，并尝试获得近似的最佳结果。 RBF是一种由高斯分布中的径向基函数的数量组成的神经网络。当给出基础函数的位置及其半径时，RRF的学习系统总结在反向矩阵的计算中。因此，学习系统非常简单，非常快。 RBF存在两个重要的问题，一个是放置基本函数并提供数据，另一个是向每个径向基函数提供适当的半径。至于第一个，我们提出了数据分布和基础函数分配方法以及自适应范围遗传算法（Arange Gas）。在这项研究中，我们将重点关注第二个问题。为此目的，我们提供基础函数的椭圆分布，并假设每个基本功能具有相同的椭圆半径。以这种方式，我们可以将半径函数的数量减少为设计变量的数量。我们将通过基准问题显示所提出的方法的有效性。

著录项

来源
《Conference of the IEEE Industrial Electronics Society, Annual》|2000年||共6页
会议地点
作者
Masao Arakawa; Hirotaka Nakayama; Ye Boon Yun; Hirochi Ishikawa;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类 TN1-53;
关键词

相似文献

外文文献
中文文献
专利

1. Optimum Design Using Radial Basis Function Network and Adaptive Range Genetic Algorithms (2nd Report, Consideration in Constraint Optimization) [J] . Masao Arakawa, Hirotaka Nakayama, Hiroshi Ishikawa 日本機械学会論文集. C編 . 2001,第655期

机译：使用径向基函数网络和自适应范围遗传算法的优化设计（第二次报告，考虑约束优化）
2. Optimum Design Using Radial Basis Function Network and Adaptive Range Genetic Algorithms (2nd Report, Consideration in Constraint Optimization) [J] . Masao Arakawa, Hirotaka Nakayama, Hiroshi Ishikawa 日本機械学会論文集. C編 . 2001,第655期

机译：使用径向基函数网络和自适应范围遗传算法的优化设计（第二次报告，考虑约束优化）
3. 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
4. Optimum design using radial basis function networks by adaptive range genetic algorithms (determination of radius in radial basis function networks) [C] . Masao Arakawa, Hirotaka Nakayama, Ye Boon Yun, Conference of the IEEE Industrial Electronics Society, Annual . 2000

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. Recursive time- and order-update algorithms for radial basis function networks. [D] . Fun, Meng Hock. 2001

机译：径向基函数网络的递归时间和顺序更新算法。
6. Hierarchical Genetic Algorithm and Fuzzy Radial Basis Function Networks for Factors Influencing Hospital Length of Stay Outliers [O] . Ahmed Belderrar, Abdeldjebar Hazzab 2017

机译：影响住院病人离群长度的因素的分层遗传算法和模糊径向基函数网络
7. Optimum Design Using Radial Basis Function Network and Adaptive Range Genetic Algorithms. 3rd Report. Usage of Data Generation by Using Self-Organizing Maps. [O] . Masao ARAKAWA, Hirotaka NAKAYAMA, Hiroshi ISHIKAWA 2002

机译：优化设计采用径向基函数网络和自适应范围遗传算法。第三次报告。使用自组织地图使用数据生成。
8. Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks [R] . Broomhead, D. S., Lowe, D. 1988

机译：径向基函数，多变量函数插值和自适应网络

Optimum design using radial basis function networks by adaptive range genetic algorithms (determination of radius in radial basis function networks)

摘要

著录项

相似文献

相关主题

期刊订阅