Function Approximation and Nonlinear Regression - MATLAB & Simulink (original) (raw)

Main Content

Create a neural network to generalize nonlinear relationships between example inputs and outputs

Apps

Functions

Examples and How To

Basic Design

• Shallow Networks for Pattern Recognition, Clustering and Time Series
  Use apps and functions to design shallow neural networks for function fitting, pattern recognition, clustering, and time series analysis.
• Fit Data with a Shallow Neural Network
  Train a shallow neural network to fit a data set.
• Create, Configure, and Initialize Multilayer Shallow Neural Networks
  Prepare a multilayer shallow neural network.
• Body Fat Estimation
  This example illustrates how a function fitting neural network can estimate body fat percentage based on anatomical measurements.
• Train and Apply Multilayer Shallow Neural Networks
  Train and use a multilayer shallow network for function approximation or pattern recognition.
• Analyze Shallow Neural Network Performance After Training
  Analyze network performance and adjust training process, network architecture, or data.
• Deploy Shallow Neural Network Functions
  Simulate and deploy trained shallow neural networks using MATLAB® tools.
• Deploy Training of Shallow Neural Networks
  Learn how to deploy training of shallow neural networks.

Training Scalability and Efficiency

• Shallow Neural Networks with Parallel and GPU Computing
  Use parallel and distributed computing to speed up neural network training and simulation and handle large data.
• Automatically Save Checkpoints During Neural Network Training
  Save intermediate results to protect the value of long training runs.
• Optimize Neural Network Training Speed and Memory
  Make neural network training more efficient.
• Train Shallow Networks on CPUs and GPUs
  Speed up training and simulation of large problems using shallow networks.

Optimal Solutions

• Choose Neural Network Input-Output Processing Functions
  Preprocess inputs and targets for more efficient training.
• Configure Shallow Neural Network Inputs and Outputs
  Learn how to manually configure the network before training using theconfigure function.
• Divide Data for Optimal Neural Network Training
  Use functions to divide the data into training, validation, and test sets.
• Choose a Multilayer Neural Network Training Function
  Comparison of training algorithms on different problem types.
• Improve Shallow Neural Network Generalization and Avoid Overfitting
  Learn methods to improve generalization and prevent overfitting.
• Train Neural Networks with Error Weights
  Learn how to use error weighting when training neural networks.
• Normalize Errors of Multiple Outputs
  Learn how to fit output elements with different ranges of values.

Concepts

• Workflow for Neural Network Design
  Learn the primary steps in a neural network design process.
• Four Levels of Neural Network Design
  Learn the different levels of using neural network functionality.
• Multilayer Shallow Neural Networks and Backpropagation Training
  Workflow for designing a multilayer shallow feedforward neural network for function fitting and pattern recognition.
• Multilayer Shallow Neural Network Architecture
  Learn the architecture of a multilayer shallow neural network.
• Understanding Shallow Network Data Structures
  Learn how the format of input data structures affects the simulation of networks.
• Sample Data Sets for Shallow Neural Networks
  List of sample data sets to use when experimenting with shallow neural networks.
• Neural Network Object Properties
  Learn properties that define the basic features of a network.
• Neural Network Subobject Properties
  Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.