Function Approximation, Clustering, and Control
Perform regression, classification, clustering, and model nonlinear dynamic systems using shallow neural networks
Generalize nonlinear relationships between example inputs and outputs, perform unsupervised learning with clustering and autoencoders.
Dynamic neural networks including NARX and Time-Delay; create Simulink® models; control nonlinear systems using model-predictive, NARMA-L2, and model-reference neural networks.
- Function Approximation and Clustering
Perform regression, classification, and clustering using shallow neural networks
- Time Series and Control Systems
Model nonlinear dynamic systems using shallow networks; make predictions using sequential data.