基于矩形函数系的模糊神经网络及其应用

提出了一种基于矩形函数系的模糊神经网络，其特点是输入空间被划分为许多大小一致且互不相交的超立方体，而每一个超立方体中的模糊神经网络都具有常值输出，只有当一个超立方体内落入样本时，相应的样栈才被编码后作为“规则”记忆下来。因此，这种网络可以自动生成模糊规则，关于静态误差的定理表明，将这种网络用于一类非线笥系统的在线控制中，当输入空间的划分足够小时，可使非线性系统的静态误差任意小，仿真实全怨表明，将这

Fuzzy Neural Network Using Rectangle Functions

Fuzzy neural network(FNN) using rectangle functions is constructed by partitioning input space into many disjoint hyper cubes with the same size. FNN is constant in each of the hyper cubes.If andonly if an input sample drops into a hyper cube would the corresponding sample be memorized through coding. Moreover,FNN can generate fuzzy rules automatically.For the control of a nonlinear system,a theorem about static error shows that static error can be decreased for small enough partition of the input space.Simulation example shows that the result is satisfactory.