Neural Network Toolbox

Network Architectures

Neural Network Toolbox supports a variety of supervised and unsupervised network architectures. With the toolbox’s modular approach to building networks, you can develop custom network architectures for your specific problem. You can view the network architecture including all inputs, layers, outputs, and interconnections.

Supervised Networks

Supervised neural networks are trained to produce desired outputs in response to sample inputs, making them particularly well-suited to modeling and controlling dynamic systems, classifying noisy data, and predicting future events.

Neural Network Toolbox includes four types of supervised networks: feedforward, radial basis, dynamic, and learning vector quantization.

Feedforward networks have one-way connections from input to output layers. They are most commonly used for prediction, pattern recognition, and nonlinear function fitting. Supported feedforward networks include feedforward backpropagation, cascade-forward backpropagation, feedforward input-delay backpropagation, linear, and perceptron networks.

A two-layer feedforward network with sigmoid hidden neurons and linear output neurons.
A two-layer feedforward network with sigmoid hidden neurons and linear output neurons. This type of network can fit multidimensional mapping problems arbitrarily well, given consistent data and enough neurons in its hidden layer.

Radial basis networks provide an alternative, fast method for designing nonlinear feedforward networks. Supported variations include generalized regression and probabilistic neural networks.

Dynamic networks use memory and recurrent feedback connections to recognize spatial and temporal patterns in data. They are commonly used for time-series prediction, nonlinear dynamic system modeling, and control systems applications. Prebuilt dynamic networks in the toolbox include focused and distributed time-delay, nonlinear autoregressive (NARX), layer-recurrent, Elman, and Hopfield networks. The toolbox also supports dynamic training of custom networks with arbitrary connections.

Maglev Modeling with Neural Time Series App 5:28
Model the position of a levitated magnet as current passes through an electromagnet beneath it.

Learning vector quantization (LVQ) networks use a method for classifying patterns that are not linearly separable. LVQ lets you specify class boundaries and the granularity of classification.

Unsupervised Networks

Unsupervised neural networks are trained by letting the network continually adjust itself to new inputs. They find relationships within data and can automatically define classification schemes.

Neural Network Toolbox includes two types of self-organizing, unsupervised networks: competitive layers and self-organizing maps.

Competitive layers recognize and group similar input vectors, enabling them to automatically sort inputs into categories. Competitive layers are commonly used for classification and pattern recognition.

Self-organizing maps learn to classify input vectors according to similarity. Like competitive layers, they are used for classification and pattern recognition tasks; however, they differ from competitive layers because they are able to preserve the topology of the input vectors, assigning nearby inputs to nearby categories.

A self-organizing map consisting of a competitive layer.
A self-organizing map consisting of a competitive layer that can classify a data set of vectors with any number of dimensions into as many classes as the layer has neurons.
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