While MV logic provides a powerful framework for capturing the logical structure of cellular automata, it has certain limitations when applied to more complex systems. One limitation is the assumption of discrete truth values, which may not fully capture the continuous nature of some dynamical systems. Additionally, the fixed cardinality of the state set in MV logic may restrict its applicability to systems with varying or continuous state spaces. To overcome these limitations and further generalize the framework, extensions to MV logic can be explored. This could involve incorporating fuzzy logic to handle uncertainty and vagueness in the logical rules extracted from neural networks. By introducing fuzzy sets and fuzzy logic operators, the framework can better model the nuanced relationships present in complex systems. Furthermore, integrating probabilistic reasoning and Bayesian logic can enhance the framework's ability to capture probabilistic dependencies and infer logical rules from uncertain data. By extending MV logic with these advanced techniques, the framework can be more versatile and adaptable to a wider range of dynamical systems.