Chemometrics and Instrumental Analysis
偏最小二乘回归径向基函数神经网络
Partial Least Squares Regression Radial Basis Function Neural NetworksA hybrid modeling approach combining Partial Least Squares Regression (PLS) with Radial Basis Function (RBF) neural networks to predict the composition of intact fresh maize kernels using Near-Infrared (NIR) spectroscopy data. / 一种结合偏最小二乘回归（PLS）与径向基函数（RBF）神经网络的混合建模方法，用于利用近红外（NIR）光谱数据预测完整鲜玉米粒的成分。
- Knowledge type
- 工艺技术
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-11-05 22:05:30
Sub-knowledge points
Rapid and accurate predictionMulti-constituent analysisIntact fresh maize kernel analysis
Higher-order knowledge points
Main parameters and performance indicators
NIR spectroscopy: Input spectral dataPLS: Dimensionality reduction techniqueRBF: Nonlinear activation functionMulti-constituent prediction: Output target
Open problems
No unresolved problem recorded yet
Related literature
ranked by community starsRapid and accurate PLS-RBF neural networks for multi-constituent prediction in intact fresh maize kernels using NIR spectroscopyOpen source
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1 recordsRecord title: Partial Least Squares Regression Radial Basis Function Neural Networks.
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