计算机应用
計算機應用
계산궤응용
COMPUTER APPLICATION
2010年
4期
985-989
,共5页
函数依赖%属性依赖%属性空间%高维映射%复合型神经网络
函數依賴%屬性依賴%屬性空間%高維映射%複閤型神經網絡
함수의뢰%속성의뢰%속성공간%고유영사%복합형신경망락
functional dependency%attribute dependency%attribute space%high-dimensional mapping%composite neural network
神经网络的优化方法一般仅局限于学习算法、输入属性方面.由于神经网络拟合的高维映射存在复杂的内在属性依赖关系,而传统的优化方法却没有对其进行分析研究.以函数依赖理论为基础,提出了属性依赖理论,阐述了属性依赖的有关定义,证明了相关定理;并且与径向基函数(RBF)神经网络结合,提出了基于属性依赖理论的RBF神经网络结构优化方法(ADO-RBF).最后通过实例证明了该方法在实际应用中的可行性.
神經網絡的優化方法一般僅跼限于學習算法、輸入屬性方麵.由于神經網絡擬閤的高維映射存在複雜的內在屬性依賴關繫,而傳統的優化方法卻沒有對其進行分析研究.以函數依賴理論為基礎,提齣瞭屬性依賴理論,闡述瞭屬性依賴的有關定義,證明瞭相關定理;併且與徑嚮基函數(RBF)神經網絡結閤,提齣瞭基于屬性依賴理論的RBF神經網絡結構優化方法(ADO-RBF).最後通過實例證明瞭該方法在實際應用中的可行性.
신경망락적우화방법일반부국한우학습산법、수입속성방면.유우신경망락의합적고유영사존재복잡적내재속성의뢰관계,이전통적우화방법각몰유대기진행분석연구.이함수의뢰이론위기출,제출료속성의뢰이론,천술료속성의뢰적유관정의,증명료상관정리;병차여경향기함수(RBF)신경망락결합,제출료기우속성의뢰이론적RBF신경망락결구우화방법(ADO-RBF).최후통과실예증명료해방법재실제응용중적가행성.
Neural network optimization methods are generally confined to learning algorithms and input attributes. Due to the high-dimensional mapping that neural network fits contains complex intrinsic attribute dependencies, the traditional optimization methods have not conducted the analytical study on it. The article put forward the attribute dependency theory based on functional dependency theory, elaborated the definition of the attribute dependency theory, and proved related theorem. Combining the Radius Basis Function (RBF) neural network, a new neural network optimization method based on attribute dependency theory (ADO-RBF) was proposed.
