FeedforwardNetzwerke

FeedforwardNetzwerke, also known as feedforward neural networks, are a fundamental type of artificial neural network. In these networks, information flows in only one direction, from the input layer, through one or more hidden layers, to the output layer. There are no cycles or loops, meaning a node in a subsequent layer can only receive input from nodes in preceding layers, not from nodes in the same or later layers.

The basic structure consists of interconnected nodes, often called neurons, organized into layers. Each connection between neurons has an associated weight, which is adjusted during the training process. Neurons within a layer typically perform a simple computation, such as a weighted sum of their inputs, followed by an activation function that introduces non-linearity. This non-linearity is crucial for the network to learn complex patterns.

Feedforward networks are trained using algorithms like backpropagation. During training, the network is presented with input data and its corresponding desired output. The network's actual output is compared to the desired output, and the error is calculated. This error is then propagated backward through the network to adjust the weights of the connections, aiming to minimize future errors.

These networks are widely used for various tasks, including classification, regression, and pattern recognition. Simple feedforward networks with a single hidden layer can approximate any continuous function, a property known as the universal approximation theorem. More complex architectures with multiple hidden layers, known as deep feedforward networks or deep neural networks, have achieved state-of-the-art performance in many fields, such as image recognition and natural language processing.