本文研究一类高阶线性齐次与非齐次迭代级整函数系数微分方程解的增长性问题。
In this paper, we investigate growth problems of solutions of a type of homogeneous and non-homogeneous higher order linear differential equations with entire coefficients of iterated order.
这种方法充分利用了神经网络的非线性函数逼近能力，构造神经网络自整定PID控制器。
Then the Neural Network PID control is realised in the model. This method makes full use of nonlinear function approximation of the Neural Network.
研究了一类整系数二阶线性微分方程解的幂和导函数的不动点和超级等问题，得到了一些精确的估计。
By using the factorization of meromorphic functions and the growth estimation of the modual, we obtain precise estimation of the order of growth and hyper-order of solutions of the equations.
研究了一类整系数二阶线性微分方程解的幂和导函数的不动点和超级等问题，得到了一些精确的估计。
By using the factorization of meromorphic functions and the growth estimation of the modual, we obtain precise estimation of the order of growth and hyper-order of solutions of the equations.
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