Physics

Control engineering

Feed forward

Evolutionary biology

Biology

Operating system

Algorithm

Optics

Recurrent neural network

Engineering

Adaptation (eye)

Function (biology)

Types of artificial neural networks

Artificial intelligence

Backpropagation

Computer science

Process (computing)

Artificial neural network

Rprop

A learning algorithm for multilayer feedforward networks, RPROP (resilient propagation), is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behavior of the error function. Contrary to other adaptive techniques, the effect of the RPROP adaptation process is not blurred by the unforeseeable influence of the size of the derivative, but only dependent on the temporal behavior of its sign. This leads to an efficient and transparent adaptation process. The capabilities of RPROP are shown in comparison to other adaptive techniques.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A direct adaptive method for faster backpropagation learning: the RPROP algorithm