In this paper, the non-fragile dissipative state estimation is addressed for semi-Markov jump inertial neural networks with reaction-diffusion. A semi-Markov jump model is used to describe the stochastic jump parameters in networks. Different from the invariable transition probabilities in the traditional Markov jump systems, the transition probabilities of the semi-Markov jump systems rely on the stochastic sojourn-time. Accordingly, the Weibull distribution taking the place of the exponential distribution in this paper is adopted for the sojourn-time of each mode in the system. Firstly, by utilizing an applicable vector substitution, the second-order differential system could be converted into the first-order one. Afterwards, via constructing a seemly Lyapunov function of the semi-Markov inertial neural networks and adequately taking advantage of the peculiarities of cumulative distribution functions, some sufficient conditions with less conservatism are constructed to assure that the estimation error system is strictly $(R_1,R_2,R_3)-\varrho -$dissipative stochastically stable. Based on these conditions, mode-dependent estimator gains are designed. Finally, a numerical example is proposed to validate the availability of the provided approach.

在本文中，针对具有反应扩散的半马尔可夫跳跃惯性神经网络解决了非脆弱耗散状态估计。半马尔可夫跳跃模型用于描述网络中的随机跳跃参数。与传统马尔可夫跳跃系统中的不变转移概率不同，半马尔可夫跳跃系统的转移概率依赖于随机逗留时间。因此，系统中各模态的停留时间采用威布尔分布代替本文中的指数分布。首先，利用一个适用的向量代换，可以将二阶微分系统转换为一阶微分系统。然后，$({\mathrm{电阻}}_1,{\mathrm{电阻}}_2,{\mathrm{电阻}}_3)-ㄧ-$耗散随机稳定。基于这些条件，设计了依赖于模式的估计器增益。最后，提出了一个数值例子来验证所提供方法的可用性。