论文部分内容阅读
为提高高阶非常模信号的均衡效果,提出了量子人工鱼群优化的自适应最小熵盲均衡算法。该算法利用超指数迭代加快了自适应最小熵盲均衡算法的收敛速度;利用薛定谔方程设计量子粒子群模型的思想,设计了快速全局寻优能力的量子人工鱼群模型,减小了超指数自适应最小熵盲均衡算法的稳态误差。仿真结果表明:与自适应最小熵盲均衡算法、超指数自适应最小熵盲均衡算法相比,量子人工鱼群优化的自适应最小熵盲均衡算法收敛速度快、稳态误差小,有利于提高通信质量。
In order to improve the equalization effect of high-order non-modal signals, an adaptive minimum entropy blind equalization algorithm based on quantum artificial fish swarm optimization is proposed. The algorithm accelerates the convergence rate of adaptive minimum entropy blind equalization algorithm by using exponential iteration. By using the Schrödinger equation to design quantum particle swarm optimization (PSO), a quantum artificial fish swarm model with fast global optimization ability is designed to reduce the hyperindex Steady - State Error Adapting to Minimum Entropy Blind Equalization Algorithm. The simulation results show that compared with the adaptive minimum entropy blind equalization algorithm and the over-exponential adaptive minimum entropy blind equalization algorithm, the quantum artificial fish swarm optimization adaptive minimum entropy blind equalization algorithm has the advantages of fast convergence, small steady-state error, Communication quality.