电子设计工程
電子設計工程
전자설계공정
Electronic Design Engineering
2015年
19期
79-82
,共4页
人工免疫系统%RBF网络%神经网络%学习策略
人工免疫繫統%RBF網絡%神經網絡%學習策略
인공면역계통%RBF망락%신경망락%학습책략
artificial immune system%RBF network%neural network%learning strategies
由于传统的RBF网络学习方法存在诸多的不足，本文提出基于免疫机制的三级RBF网络学习方法：在第一级得到网络隐层节点数作为疫苗，不仅可自行构建网络，还降低了第二级搜索空间的复杂度；第二级利用人工免疫算法对解空间进行多点搜索，得到全局最优的隐层非线性参数；第三级采用最小二乘法确定网络输出层线性参数，极大地降低了第二级结构的维数，提高了算法效率。经典型Hermit多项式逼近实验验证了该方法训练得到的RBF网络性能优越。
由于傳統的RBF網絡學習方法存在諸多的不足，本文提齣基于免疫機製的三級RBF網絡學習方法：在第一級得到網絡隱層節點數作為疫苗，不僅可自行構建網絡，還降低瞭第二級搜索空間的複雜度；第二級利用人工免疫算法對解空間進行多點搜索，得到全跼最優的隱層非線性參數；第三級採用最小二乘法確定網絡輸齣層線性參數，極大地降低瞭第二級結構的維數，提高瞭算法效率。經典型Hermit多項式逼近實驗驗證瞭該方法訓練得到的RBF網絡性能優越。
유우전통적RBF망락학습방법존재제다적불족，본문제출기우면역궤제적삼급RBF망락학습방법：재제일급득도망락은층절점수작위역묘，불부가자행구건망락，환강저료제이급수색공간적복잡도；제이급이용인공면역산법대해공간진행다점수색，득도전국최우적은층비선성삼수；제삼급채용최소이승법학정망락수출층선성삼수，겁대지강저료제이급결구적유수，제고료산법효솔。경전형Hermit다항식핍근실험험증료해방법훈련득도적RBF망락성능우월。
In order to improve the traditional RBF learning strategy, a three-level RBF network learning algorithm based on immune system is proposed, which can calculates the number of the hidden-layer neurons in the first level as immune vaccine, the network can be established and adjusted by itself, and the complexity of search space in the second level can be reduced. The global optimum hidden-layer nonlinear parameters are searched for in the second level by parallel searching with artificial immune algorithm. The output-layer linear parameters are estimated in the third level with least square method, which makes the design dimension of the second level decreased and the algorithm efficiency improved. The experiment of Hermit polynomial approximation shows that the performance of the RBF network trained by the algorithm is superior.
