Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

By Qunxiong Zhu, Chengfei Li. 2006

30 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering

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In Control and Systems Engineering

1. Peter R. Florence

Citations in the corpus

• DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation (2019, background): “Similar approaches have been extensively studied in [40, 7, 38], for applications including noise reduction, missing measurement completions, and fault detections.”
• Combined Input Training and Radial Basis Function Neural Networks based Nonlinear Principal Components Analysis Model Applied for Process Monitoring (2012, background): “…[ (1−η) ∂J ∂b2(k) ] +η (∆b2(k)) (26) where ∆TNℓ(k+1) = TNℓ(k+1)−TNℓ(k) (27) ∆z(k+1) = z(k+1)− z(k) (28) wherez represents each internal network parameter. where adaptive learning ratesrt , rz are adapted as proposed in (Zhu and Li, 2006). rz(k) = 2λzrz(k−1) (29) λz(k) = sign [ ∂J ∂ z(k) ∂J ∂…”