CAJ | 학술논문

前馈神经网在智能控制设计训练中收敛速度慢、易陷入局部极值，且对初始权值依赖性。为此提出一种基于超维无限折叠反转迭代混沌的蛙跳群优化算法，训练前馈神经网络参数，进行全局优化智能控制设计。算法在充分利用BP算法的误差反传信息和梯度信息的基础上，引入基于超维无限折叠反转迭代混沌的概念，结合混合蛙跳群仿生算法，将混沌蛙跳群作为全局搜索器，以梯度下降信息作为局部搜索器来调整智能控制网络的权值和阈值，实现全局寻优和空间智能搜索。实验和算法对比结果表明，新的人工神经网络智能控制算法在均方差、泛化均方差等指标上较传统算法具有明显优势。改进算法特别适合人工智能控制模型设计与预测建模，具有较高的预测精度和泛化能力，稳健性好，具有较好的全局优化自适应控制能力。
전궤신경망재지능공제설계훈련중수렴속도만、역함입국부겁치，차대초시권치의뢰성。위차제출일충기우초유무한절첩반전질대혼돈적와도군우화산법，훈련전궤신경망락삼수，진행전국우화지능공제설계。산법재충분이용BP산법적오차반전신식화제도신식적기출상，인입기우초유무한절첩반전질대혼돈적개념，결합혼합와도군방생산법，장혼돈와도군작위전국수색기，이제도하강신식작위국부수색기래조정지능공제망락적권치화역치，실현전국심우화공간지능수색。실험화산법대비결과표명，신적인공신경망락지능공제산법재균방차、범화균방차등지표상교전통산법구유명현우세。개진산법특별괄합인공지능공제모형설계여예측건모，구유교고적예측정도화범화능력，은건성호，구유교호적전국우화자괄응공제능력。
According to the feed-forward neural network in the design of the intelligent control, the convergence speed is slow, an improved frog leaping swarm optimization algorithm was proposed for training feed-forward neural networks based on the hyper space dimensional and infinite folding inversion iterative chaos mapping. The global optimal control was de-signed. The error back propagation information and gradient information of BP algorithm were made full use, and the con-cept of hyper space dimensional and infinite folding inversion iterative chaos mapping was presented. The global optimiza-tion and intelligent search were realized. The simulation and comparison results show that the algorithm has various advan-tages in the simulation parameters such as mean square error, MSE of generalization etc. It is very suitable for artificial in-telligence control modeling, the training accuracy and generalization accuracy are perfect, and it has better ability of global optimization adaptive control.