关键词: 函数依赖, 属性依赖, 属性空间, 高维映射, 复合型神经网络

Abstract: 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.

Key words: functional dependency, attribute dependency, attribute space, high-dimensional mapping, composite neural network