抄録
Higher order derivatives of Universal Learning Network (U.L.N.) has been derived by forward and backward propagation computing methods, which can model and control large scale complicated systems such as industrial plants, economic, social and life phenomena. In this paper, a new concept of nth order asymptotic orbital stability for U.L.N. is defined by using higher order derivatives of U.L.N. and sufficient condition of asymptotic orbital stability for U.L.N. is derived. It is also shown that if 3rd order asymptotic orbital stability for recurrent neural network is proved, higher order asymptotic orbital stability than 3rd order is guaranteed.
