Define new neural network architectures and algorithms
for advanced applications

`network` |
Create custom neural network |

Create and learn the basic components of a neural network object.

**Configure Neural Network Inputs and Outputs**

Learn how to manually configure the network before
training using the `configure`

function.

**Understanding Neural Network Toolbox Data Structures**

Learn how the format of input data structures affects the simulation of networks.

**Create and Train Custom Neural Network Architectures**

Customize network architecture using its properties and use and train the custom network.

**Adaptive Neural Network Filters**

Design an adaptive linear system that responds to changes in its environment as it is operating.

Learn the architecture, design, and training of perceptron networks for simple classification problems.

Learn to design and use radial basis 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.

Design a linear network that, when presented with a set of given input vectors, produces outputs of corresponding target vectors.

Design a network that stores a specific set of equilibrium points such that, when an initial condition is provided, the network eventually comes to rest at such a design point.

**Workflow for Neural Network Design**

Learn the primary steps in a neural network design process.

Learn about a single-input neuron, the fundamental building block for neural networks.

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.

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