一种优化模糊神经网络的多目标微粒群算法
Fuzzy Neural Network Optimization by a Multi-Objective Particle Swarm Optimization Algorithm
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摘要: 模糊神经网络优化是一个多目标优化问题.通过对模糊神经网络和微粒群算法的深入分析,提出了一种多目标微粒群算法.在算法中将网络的精确性和复杂性分别作为目标进行优化,再用一种启发性分量加权均值法来选取个体极值和全局极值.算法能够引导粒子较快地向非劣最优解区域移动并最终获得多个非劣最优解,为模糊神经网络的精确性和复杂性的折中寻优问题提供了一种解决方法.茶味觉信号识别的仿真实验验证了该算法的有效性.Abstract: Designing a set of fuzzy neural networks can be considered as solving a multi-objective optimization problem. In the problem, performance and complexity are two conflicting criteria. An algorithm for solving the multi objective optimization problem is presented based on particle swarm optimization through the improvement of the selection manner for global and individual extremum. The search for the Pareto optimal set of fuzzy neural networks optimization problems is performed, and a tradeoff between accuracy and complexity of fuzzy neural networks is clearly shown by obtaining non-dominated solutions. Numerical simulations for taste identification of tea show the effectiveness of the proposed algorithm.
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