基于双链量子遗传算法的过程神经元网络训练

基于函数正交基展开的过程神经元网络训练,由于参数较多BP算法不易收敛。针对这一问题,本文提出了一种基于双链量子遗传算法的解决方案。首先按权值参数的个数确定染色体上的基因数,完成种群编码,然后通过染色体评估获得当前最优染色体,以该染色体为目标,用量子旋转门完成种群中个体的更新,用量子非门实现个体变异增加种群多样性。在该方法中,每条染色体携带两条基因链,因此可扩展对解空间的遍历性,加速优化进程。以两组二维三角函数的模式分类问题为例,仿真结果表明该方法不仅收敛速度快,而且寻优能力强。

Training of Process Neural Networks Based on Quantum Genetic Algorithm with Double Chains

For training of process neural networks based on the orthogonal basis expansion,it is difficult to converge for BP algorithm as more parameters.Aiming at the issue,this paper proposes a solution based on quantum genetic algorithm with double chains.Firstly,the number of genes is determined by the number of weight parameters,quantum chromosomes are constructed by qubits,and the current optimal chromosome is obtained with the help of colony assessment.Secondly,taking each qubit in this optimal chromosome as t...