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By using semi-discrete and Takagi-Sugeno fuzzy methods, a new version of discrete analogue of stochastic fuzzy cellular neural networks is formulated, which gives a more accurate characterization for continuous-time stochastic model than that by Euler scheme. Firstly, the <inline-formula> <tex-math notation="LaTeX">$2p$ </tex-math></inline-formula>-th moment global exponential stability for the obtained semi-discrete stochastic Takagi-Sugeno fuzzy model is studied with the help of Minkowski inequality and Hölder inequality. Secondly, the <inline-formula> <tex-math notation="LaTeX">$2p$ </tex-math></inline-formula>-th mean almost periodic outputs of the model is investigated by using Krasnoselskii’s fixed point theorem. Finally, illustrative examples and numerical simulations are given to demonstrate that our results are feasible.

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$2p$ -th Moment Global Exponential Stability of a Unique $2P$ -th Mean Almost Periodic Oscillation for Semi-Discrete Takagi-Sugeno Fuzzy Stochastic Cellular Neural Networks With Time Delays