Multi-objective structure selection for radial basis function networks based on genetic algorithm
Multi-objective structure selection for radial basis function networks based on genetic algorithm
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DOI：
10.1109/cec.2003.1299790
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发表时间：
2003-12
期刊：
影响因子：
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通讯作者：
T. Hatanaka;N. Kondo;K. Uosaki
中科院分区：
文献类型：
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作者：
T. Hatanaka;N. Kondo;K. Uosaki
径向基函数（RBF）网络是一种性能良好的非线性系统建模方法。RBF网络的结构选择是一个重要的问题，但该问题的框架尚未建立。提出了一种基于多目标遗传算法的RBF网络多目标结构选择方法。遗传算法将RBF网络的结构编码到染色体中，然后对模型精度和复杂性进行多目标优化。数值仿真结果表明了该方法的有效性。
Radial basis function (RBF) network is well known as a good performance approach to nonlinear system modeling. Though structure selection of RBF network is an important issue, the framework of this problem has not been established. In this paper, we propose multiobjective structure selection method for RBF networks based on MOGA (multiobjective genetic algorithm). The structure of RBF networks is encoded to the chromosomes in GA, then evolved toward to Pareto-optimum for multiobjective functions concerned with model accuracy and complexity. Some numerical simulation results indicate the applicability of the proposed approach.