1) cyclic subspace regression
循环子空间回归
1.
A new approach, radial basis functions-cyclic subspace regression (RBF-CSR), was proposed based on the analyzing radial basis functions-patial least squares (RBF-PLS).
径向基循环子空间回归(RBFCSR)网络,保留了径向基偏最小二乘(RBFPLS)网络的优点,且可在更广的范围内选择最优模型,但仍存在着参数难以确定,计算量大等问题。
2.
Radial basis function-cyclic subspace regression (RBF-CSR) approach is a rapid one-step modeling method, escaping the difficulty of ANN architecture design.
径向基函数循环子空间回归(RBFCSR)是一种有效的非线性网络模型,以高斯条为基函数,性能更优,但其参数多,且难以选定,将显著影响模型性能。
3.
The radial basis function networks (RBFN) was combined with the cyclic subspace regression (CSR) in this paper, and a modeling approach by RBFN-CSR was designed.
本文将径向基函数网络(RBFN)与循环子空间回归(CSR)相结合,设计了RBFN-CSR建模方法。
2) Multi-Cyclic subspace Regression
多元循环子空间回归
3) Multi-dependent variables Cyclic Subspace Regression
多因变量循环子空间回归
补充资料：循环系统的进化鱼的循环系统
李瑞端绘
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说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条