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Define Neural Network Architectures

Define new neural network architectures and algorithms for advanced applications


network Create custom 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 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.

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.

Radial Basis Neural Networks

Learn to design and use radial basis networks.

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.

Linear Neural Networks

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

Hopfield Neural Network

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.

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.

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