Siemens Aktiengesellschaft

Ralph Neuneier

Volker Tresp

Hans-Georg Zimmermann

The training phase of a neural network NN is stopped before an error function, which is to be minimized in the training phase, reaches a minimum (,). A first variable (EG) is defined using, for example, the optimal brain damage method or the optimal brain surgeon method, on the assumption that the error function is at the minimum. Furthermore, a second variable (ZG) is determined which provides an indication of the manner in which the value of the error function varies when a weight (w,) is removed from the neural network (NN). The first variable (EG) and the second variable (ZG) are used to classify the weight (w,) as being suitable or unsuitable for removal from the neural network (NN).

Method for determination of weights, suitable for elimination, of a neural network using a computer

A state vector (SV,) is determined with elements that characterize a financial market (,). Taking into account predetermined evaluation variables, an evaluation (V,) is determined (,) for the state vector (SV,). In addition, a chronologically following state vector (SV,) is determined (,) and evaluated (V,). On the basis of the two evaluations (V,, V,), weights (w,) of the neural network (NN) are adapted (,) using a reinforcement learning method (&Dgr;w,).

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München, Germany

Ralph Neuneier