At the same time, the robust adaptive convergence of the kind of network model is also discussed.

With more attention paid to how to well make use of FFT and posterior information , an efficient method to process data is proposed, which leads to robust convergency of algorithm.

Moreover, the control laws using smooth time varying feedback do not stabilize the system at exponential convergence rate.

Existence and exponential convergence of periodic oscillatory solution for BAM neural networks with delays is discussed by using fixed-point theorem,constructing suitable Lyapunov functions,and some analysis techniques.

The feed-forward controller makes the practical robot trace in nominal system converge to the desired one with an exponential convergence rate,while the feedback controller makes the error be bounded in terminal.

And it is proved that all other solutions converge exponentially to the above ω-periodic solution.

In this paper, by constructing Liapunov functional and some analysis techniques, we study the periodic solutions of a neural network as follows: dxdt=-x(t)-α tanh+I1(t) dydt=-y(t)-α tanh+I2(t) and obtain some sufficient conditions to ensure the network exist a unique periodic solution, and its all solutions converge exponentially to the periodic solution.

We give some estimates about the exponent of convergence of the quasiconformal Fuchsian group G =(?)Γ(?)-1.

In this paper,we investigated the zeros of meromorphic solutions of higher order linear differential equationsf~((k))+A_(k-1)f~((k-1))+…+A_0f=F where A_0(z),A_1(z),…,A_(k-1)(z),F(z)≠0 are meromorphic functions,when A_0(z) has larger growth than any other A_j(z)(j≠0),we obtain some precise estimates of the exponent of convergence of the zero-sequence of meromorphic solutions for the above equation.

The hyper order, the exponent of convergence and the hyper-exponent of convergence of zeros of solutions for some types of K-order linear differential equations with entire coefficients are discussed.