Define Shallow Neural Network Architectures - MATLAB & Simulink (original) (raw)

Main Content

Define shallow neural network architectures and algorithms

Functions

Examples and How To

Custom Neural Networks

• Create Neural Network Object
  Create and learn the basic components of a neural network object.
• Configure Shallow Neural Network Inputs and Outputs
  Learn how to manually configure the network before training using theconfigure function.
• Understanding Shallow Network Data Structures
  Learn how the format of input data structures affects the simulation of networks.
• Edit Shallow Neural Network Properties
  Customize network architecture using its properties and use and train the custom network.

Historical and Alternative Neural Networks

• Adaptive Neural Network Filters
  Design an adaptive linear system that responds to changes in its environment as it is operating.
• Perceptron Neural Networks
  Learn the architecture, design, and training of perceptron networks for simple classification problems.
• Classification with a Two-Input Perceptron
  A two-input hard limit neuron is trained to classify four input vectors into two categories.
• Outlier Input Vectors
  A 2-input hard limit neuron is trained to classify 5 input vectors into two categories.
• Normalized Perceptron Rule
  A 2-input hard limit neuron is trained to classify 5 input vectors into two categories.
• Linearly Non-separable Vectors
  A 2-input hard limit neuron fails to properly classify 5 input vectors because they are linearly non-separable.
• Radial Basis Neural Networks
  Learn to design and use radial basis networks.
• Radial Basis Approximation
  This example uses the NEWRB function to create a radial basis network that approximates a function defined by a set of data points.
• Radial Basis Underlapping Neurons
  A radial basis network is trained to respond to specific inputs with target outputs.
• Radial Basis Overlapping Neurons
  A radial basis network is trained to respond to specific inputs with target outputs.
• GRNN Function Approximation
  This example uses functions NEWGRNN and SIM.
• PNN Classification
  This example uses functions NEWPNN and SIM.
• Probabilistic Neural Networks
  Use probabilistic neural networks for classification problems.
• Generalized Regression Neural Networks
  Learn to design a generalized regression neural network (GRNN) for function approximation.
• Learning Vector Quantization (LVQ) Neural Networks
  Create and train a Learning Vector Quantization (LVQ) Neural Network.
• Learning Vector Quantization
  An LVQ network is trained to classify input vectors according to given targets.
• Linear Neural Networks
  Design a linear network that, when presented with a set of given input vectors, produces outputs of corresponding target vectors.
• Linear Prediction Design
  This example illustrates how to design a linear neuron to predict the next value in a time series given the last five values.
• Adaptive Linear Prediction
  This example shows how an adaptive linear layer can learn to predict the next value in a signal, given the current and last four values.

Concepts

• Workflow for Neural Network Design
  Learn the primary steps in a neural network design process.
• Neuron Model
  Learn about a single-input neuron, the fundamental building block for neural networks.
• Neural Network Architectures
  Learn architecture of single- and multi-layer networks.