In cross-cultural communication, multimedia animation is crucial in defining a nation's image and cultural form. It is a key vehicle for cultural diffusion and a tool for film and television to convey national cul...
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In cross-cultural communication, multimedia animation is crucial in defining a nation's image and cultural form. It is a key vehicle for cultural diffusion and a tool for film and television to convey national culture and highlight regional culture. Animation's particular charm, position, and function in cultural dissemination are further highlighted by the special cinematic language used to portray emotions, and it also reflects the medium's unique significance in the rapidly evolving modern society. To better understand the emotions generated by various multimedia animations, in-depth research is needed. To investigate these issues, this article explores the use of the sigma-piartificialneuralnetwork (SP-ANN) algorithm based on the grey wolf optimization algorithm (GWOA) to identify emotional states. Compared with traditional sigma n-artificialneuralnetworkalgorithms, a training process that does not require complex derivative calculations in derivative-based algorithms is performed. sigma n-networks can benefit from the proposed learning algorithms. This algorithm has high approximation accuracy and is particularly suitable for real-time approximation of nonlinear processes. The test results indicate that the proposed algorithm can work as expected.
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