摘要
结合自适应谐振理论和域理论的优点 ,针对回归估计问题的特性 ,提出了一种新型神经网络回归估计算法 FTART3.该算法学习速度快、归纳能力强 ,不仅具有增量学习能力 ,还克服了 BP类算法需要人为设置隐层神经元的缺陷 .直线、正弦、二维墨西哥草帽、三维墨西哥草帽等 4个实验表明 ,FTART3在函数近似效果和训练时间代价上都优于目前常用于回归估计问题的
Through combining the advantages of field theory with adaptive resonance theory and contraposing the characteristics of regression estimate problem, a novel neural network regression estimate algorithm FTART3 is proposed in this paper. This algorithm achieves fast learning speed and strong generalization ability. It not only has incremental learning ability but also overcomes the defect of manually configuring hidden neurons that is necessary for BP kind algorithms. Experiments including line, sine, 2D Mexican hat and 3D Mexican hat show that FTART3 is superior to BP kind algorithms, which are often used in regression estimate, on both function approximation effect and training time cost.
出处
《计算机学报》
EI
CSCD
北大核心
2000年第6期654-659,共6页
Chinese Journal of Computers
基金
国家自然科学基金!( 69875 0 0 6.A)
江苏省自然科学基金!( BK990 3 6)
关键词
神经网络
回归估计
域理论
自适应谐振理论
算法
neural networks, regression estimate, field theory, adaptive resonance theory
