手术治疗46例胸腰椎爆裂骨折的临床分析

目的探讨前路、后路、前后路联合手术治疗胸腰椎爆裂骨折的特点。方法依据对患者所选择的前路、后路、前后路联合手术术式分为三组,进行影像学评价和神经功能评价。结果各组末次随访脊髓功能评价分级提高情况采用R×C表x2检查方法进行统计学分析,各组差异无显著意义。各组术后cobb氏角改善率应用秩和检验方法进行统计学分析,各组差异无显著意义。结论我们综合应用Denis和McAfee的分型,结合了骨折形态、损伤机制和稳定性评价,对胸腰椎爆裂骨折的治疗有较好的指导意义。手术方式的选择更多基于脊柱机械性稳定性、神经性稳定性评价。综合考虑骨折部位、骨折后时间、患者年龄、工种以及术者对入路的熟悉程度等。

Earth slope reliability analysis under seismic loadings using neural network

A new method was proposed to cope with the earth slope reliability problem under seismic loadings. The algorithm integrates the concepts of artificial neural network, the first order second moment reliability method and the deterministic stability analysis method of earth slope. The performance function and its derivatives in slope stability analysis under seismic loadings were approximated by a trained multi-layer feed-forward neural network with differentiable transfer functions. The statistical moments calculated from the performance function values and the corresponding gradients using neural network were then used in the first order second moment method for the calculation of the reliability index in slope safety analysis. Two earth slope examples were presented for illustrating the applicability of the proposed approach. The new method is effective in slope reliability analysis. And it has potential application to other reliability problems of complicated engineering structure with a considerably large number of random variables.

Global exponential stability of interval neural networks with a fixed delay

The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). The results obtained provide an easily verified guideline for determining the exponential robust stability of delayed neural networks. The theoretical analysis and numerical simulations show that the results are less conservative and less restrictive than those reported recently in the literature.

Delay-Dependent Exponential Stability of Stochastic Delayed Recurrent Neural Networks with Markovian Switching

The exponential stability problem is investigated for a class of stochastic recurrent neural networks with time delay and Markovian switching. By using Ito＇s differential formula and the Lyapunov stability theory, sufficient condition for the solvability of this problem is derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example and the simulation are exploited to demonstrate the effectiveness of the proposed results.

Stability switches and Bogdanov-Takens bifurcation in an inertial two-neuron coupling system with multiple delays

In this paper,we investigate an inertial two-neural coupling system with multiple delays.We analyze the number of equilibrium points and demonstrate the corresponding pitchfork bifurcation.Results show that the system has a unique equilibrium as well as three equilibria for different values of coupling weights.The local asymptotic stability of the equilibrium point is studied using the corresponding characteristic equation.We find that multiple delays can induce the system to exhibit stable switching between the resting state and periodic motion.Stability regions with delay-dependence are exhibited in the parameter plane of the time delays employing the Hopf bifurcation curves.To obtain the global perspective of the system dynamics,stability and periodic activity involving multiple equilibria are investigated by analyzing the intersection points of the pitchfork and Hopf bifurcation curves,called the Bogdanov-Takens(BT)bifurcation.The homoclinic bifurcation and the fold bifurcation of limit cycle are obtained using the BT theoretical results of the third-order normal form.Finally,numerical simulations are provided to support the theoretical analyses.

A Novel Neural Network for Linear Complementarity Problems

In this paper, we present a neural network for solving linear complementarity problem in real time. It possesses a very simple structure for implementation in hardware. In the theoretical aspect, this network is different from the existing networks which use the penalty functions or Lagrangians. We prove that the proposed neural network converges globally to the solution set of the problem starting from any initial point. In addition, the stability of the related differential equation system is analyzed and five numerical examples are given to verify the validity of the neural network.

STABILITY FOR THE MIX-DELAYED COHEN-GROSSBERG NEURAL NETWORKS WITH NONLINEAR IMPULSE

In this paper,the authors are concerned with the stability of the mix-delayed Cohen-Grossbergneural networks with nonlinear impulse by the nonsmooth analysis.Some novel sufficientconditions are obtained for the existence and the globally asymptotic stability of the unique equilibriumpoint,which include the well-known results on some impulsive systems and non-impulsive systems asits particular cases.The authores also analyze the globally exponential stability of the equilibriumpoint.Two examples are exploited to illustrate the feasibility and effectiveness of our results.

Global robust stability of complex-valued recurrent neural networks with time-delays and uncertainties

This paper focuses on the existence, uniqueness and global robust stability of equilibrium point for complex-valued recurrent neural networks with multiple time-delays and under parameter uncertainties with respect to two activation functions. Two sufficient conditions for robust stability of the considered neural networks are presented and established in two new time-independent relationships between the network parameters of the neural system, Finally, three illustrative examples are given to demonstrate the theoretical results.

Exponential stability of memristor-based synchronous switching neural networks with time delays

In this paper, we study the existence, uniqueness and stability of memristor-based syn- chronous switching neural networks with time delays. Several criteria of exponential stability are given by introducing multiple Lyapunov functions. In comparison with the existing publications on simplice memristive neural networks or switching neural net- works, we consider a system with a series of switchings, these switchings are assumed to be synchronous with memristive switching mechanism. Moreover, the proposed stability conditions are straightforward and convenient and can reflect the impact of time delay on the stability. Two examples are also presented to illustrate the effectiveness of the theoretical results.