A Joint Optimization of Momentum Item and Levenberg-Marquardt Algorithm to Level Up the BPNN’s Generalization Ability

Abstract EN

Back propagation neural network (BPNN) as a kind of artificial neural network is widely used in pattern recognition and trend prediction.

For standard BPNN, it has many drawbacks such as trapping into local optima, oscillation, and long training time.

Because training the standard BPNN is based on gradient descent method, and the learning rate is fixed.

Momentum item and Levenberg-Marquardt (LM) algorithm are two ways to adjust the weights among the neurons and improve the BPNN’s performance.

However, there is still much space to improve the two algorithms.

The hybrid optimization of damping factor of LM and the dynamic momentum item is proposed in this paper.

The improved BPNN is validated by Fisher Iris data and wine data.

Then, it is used to predict the visit_spend.

The database is provided by Dunnhumby's Shopper Challenge.

Compared with the other two improved BPNNs, the proposed method gets a better performance.

Therefore, the proposed method can be used to do the pattern recognition and time series prediction more effectively.