A HYBRID LEARNING RULE FOR A FEEDFORWARD NETWORK

In feedforward networks trained by the classic backpropagation algorithm, as introduced by Rumelhart et al., the weights are modified according to the method of steepest descent. The goal of this weight modification is to minimise the error in network-outputs for a given training set.

Basing upon Jacobs’ work, we point out drawbacks of steepest descent and suggest improvements on it. These yield a feedforward network, which adjusts its weights according to a (globally convergent) parallel coordinate descent method.

Then we combine this parallel coordinate descent with a hybrid learning rule from -rule and momentum version. For adjusting the parameters of this rule we use a Sugeno/Tagaki fuzzy controller. We only need four rules for the controlling process. Because of using the Sugeno/Takagi controller, no defuzzification has to be performed and the controller works faster than the familiar one.

We conclude that this algorithm is very suitable for fast training and global convergence.