Refinements in training schemes for the Coulomb Energy network

We discuss the interesting perspective offered by the Coulomb Energy network and we identify certain disadvantages with the existing approach to training it. We address these problems by constraining its architecture (topology) and offer a derivation of the new associated training algorithm. We study further refinements of this algorithm. Most notably, existing genetic algorithms are employed as initial search techniques and simulation results are provided.