Training of process neural networks based on improved quantum genetic algorithm

Aiming at the problem that it is difficult for BP algorithm to converge because of more parameters in training of process neural networks based on orthogonal basis expansion, a solution on the basis of an improved quantum genetic algorithm is proposed in the paper. An improved quantum genetic algorithm based on Bloch coordinates of qubits is proposed, which is integrated into the training of process neural networks. The number of genes on a chromosome is determined by the number of weight parameters and population coding is completed. Individuals in the population are updated by new quantum rotation gate. In this method, each chromosome carries three chains of genes, so can extend ergodicity for solution space and accelerate optimization process. Taking the pattern classification of two groups of two-dimensional trigonometric functions as an example, the simulation results show that the method has not only fast convergence but also good optimization ability.