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 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 supports four types of supervised networks: 

• 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.
• 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.
• Learning vector quantization (LVQ) is a powerful 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 supports two types of self-organizing, unsupervised networks:

• 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.

Neural networks for pattern recognition (top left), clustering (top right), and time series data fitting (bottom). Neural Network Toolbox lets you design various types of networks and visualize their architectures.