基于分组混沌PSO算法的模糊神经网络建模研究

为改善记忆功放建模的精度，且针对粒子群算法早期收敛速度较快，但在后期易陷入早熟收敛，局部最优等特点，提出了一种分组并行混沌粒子群优化算法（Grouping Parallel-Chaotic Particle Swarm Optimization，GP-CPSO），将分组粒子群优化算法与混沌思想相结合，并用该算法优化动态模糊神经网络（Dynamic Fuzzy Neural Network，DFNN）参数，建立DFNN功放模型。引入分组的CPSO群算法，将种群划分为若干个组，每组单独计算，大大提高了收敛速度，同时将混沌思想运用到每个粒子当中去，避免早熟和局部最优，缩短了迭代时间。通过仿真结果可以看到，GP-CPSO优化后的动态模糊神经网络建模的训练误差减小到0.1以内，收敛速度提高32.5%，从而验证了这种建模方法有效且可靠。

Fuzzy neural network for amplifier power modeling based on grouping parallel-chaotic Particle Swarm Optimization

In order to improve the accuracy of radio frequency power amplifier with memory effect, and the early fast convergence rate of the traditional particle swarm optimization algorithm, but in the later period easy to fall into premature and local optimum characteristics, a group of parallel chaotic particle swarm optimization algorithm is proposed and the dynamic fuzzy neural network parameters are optimized by using the algorithm to optimize the dynamic fuzzy neural network parameters. The grouping parallel chaotic particle swarm optimization algorithm is used to combine the grouping method and chaotic particle swarm optimization algorithm. The population can be divided into several groups. Each group computes independently to improve the convergence rate, while the chaos theory is applied to each particle to avoid premature and local optimum, shortening the time of iteration. By the simulation, the training error of the model is reduced to 0.1, and the convergence rate is improved by 32.5%, which verifies the validity and reliability of the method.