Feed-forward

Feed-forward and recurrent networks.
In a feed-forward network, links are unidirectional, and there are no cycles.
In a recurrent network, the links can form arbitrary topologies.

Feed-forward neural network regression Basis function regression with adaptive basis functions. Given a measurement vector, each layer of the network makes a linear transformation and then applies a nonlinearity to each vector component.

Feed-forward neural networks, where the data ow from input to output units is strictly feedforward.

Feed-forward Networks have become increasingly popular in feature recognition and function mapping (approximation) problems in a wide area of applications.

Although the feed-forward network architectures are most employed in engineering applications, allowing some down-stream neurons to connect to up-stream ones adds an interesting feature - dynamics.

It could only run feed-forward networks.
Expensive: ~$2k per chip, ~$10k for full development system including software and EEPROM
Adaptive Solutions CNAPS (more details later) has done better (although not spectacular).

An autoencoder is a feed-forward neural network which is trained to approximate the identity function. That is, it is trained to map from a vector of values to the same vector.

Simulating a feed-forward network is an extremely fast process, since it involves mainly multiplications.

Multi-Layer Perceptron (MLP) with sigmoidal Feed-Forward and standard Back-Propagation (BP) learning algorithm was employed as a forecaster for bacteria-antibiotic interactions of infectious diseases (Abidi and Goh, 1998).

The topic of generalization is also covered on the page: Exact Representations from Feed-Forward Neural Networks
The Size of the Network ...

Current neural net technology focusses unduly on simple units and feed-forward architectures, rather than units with (say) refractory periods and dynamic connectivity.

with similar features can be used, most commonly tanh() which has an output range of [-1,1]. The sigmoid function has the additional benefit of having an extremely simple derivative function for backpropagating errors through a feed-forward neural ...

See also: Neural network, Classification, Artificial neural network, Artificial intelligence, Percept