# Define Shallow Neural Network Architectures

Define shallow neural network architectures and algorithms

## Functions

`network` | Create custom shallow neural network |

## 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 the`configure`

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.

**Custom Neural Network Helper Functions**Use template functions to create custom functions that control algorithms to initialize, simulate, and train your networks.