Abstract
To enhance the approximation ability of process neural networks, a novel training algorithm is proposed by employing an improved quantum genetic algorithm. The proposed approach is applied to the training of process neural networks. The number of genes in a single chromosome is equal to the number of weight parameters. Taking each qubit in the current optimal chromosome as the goal, all individuals are updated by quantum rotation gate. In this method, each chromosome has three chains of genes, which can accelerate convergence. Taking the pattern classification of trigonometric functions as an example, the experimental results show that the proposed method is obviously superior to the common process neural networks.
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