This research introduces three novel zeroing neural network (ZNN) models for addressing the time-varying Yang–Baxter-like matrix equation (TV-YBLME) with arbitrary (regular or singular) real time-varying (TV) input matrices in continuous time. One ZNN dynamic utilizes error matrices directly arising from the equation involved in the TV-YBLME. Moreover, two ZNN models are proposed using basic properties of the YBLME, such as the splitting of the YBLME and sufficient conditions for a matrix to solve the YBLME. The Tikhonov regularization principle enables addressing the TV-YBLME with an arbitrary input real TV matrix. Numerical experiments, including nonsingular and singular TV input matrices, show that the suggested models deal effectively with the TV-YBLME.

The animation industry is an important part of China’s cultural and creative industries. In fact, it is the leading cultural and creative industry in China. However, there is insufficient research on the audience’s views in China’s animation industry, which has become an important research gap. Thus, an integrated approach of FAHP and GRA is proposed in this study, to analyse and evaluate the key selection factors for the Chinese animation industry from the perspective of a Chinese audience. In this research, in both FAHP and GRA models, factors such as visual appealing character, interesting performance of character animation, and easy-to-understand storyline are prioritised conditions for the selection of Chinese animation from the perspective of Chinese audiences. The main contribution of this research is to underscore the value of the hybrid MCDM model to aid Chinese animation companies in aligning their productions with audience expectations and making informed decisions. Finally, this study offers a systematic and objective model for Chinese animation selection, providing practical insights that can be applied in the industry and can serve as a valuable reference for future research in similar domains.