Industry 4.0 is the fourth industrial revolution led by intelligent manufacturing. The factory integrates production equipment, wireless signal connections, and sensors into a complex ecosystem platform that can supervise the entire production line process and execute decisions autonomously. During the process of the industrial revolution, a lot of complex industry data is accumulated. A major challenge in Industry 4.0 involves coping with the uncertainty, imprecision, and incompleteness of these data. The representation of fuzzy linguistic terms based on fuzzy theory provides a straightforward framework for building more understandable, imprecision-aware decision support systems

Up to now, although fuzzy systems have been widely used for industry data analysis, there are substantial research challenges and open questions regarding optimization to be explored. Scientific programming methods including evolutionary computation, multiobject optimization, and sparse optimization have been successfully and efficiently applied in many fields. Thus, designing an efficient and effective fuzzy system with scientific programming methods to deal with uncertainty is an emerging and promising topic to improve reasoning and intelligent monitoring, control, diagnostics, and optimization in Industry 4.0.

This Special Issue aims to collate original research and review articles that face contemporary research issues of fuzzy systems modeling of complex industry data.

Potential topics include but are not limited to the following:

• Scientific programming for fuzzy rule reduction
• Scientific programming for high-efficiency antecedents and consequent learning
• Fuzzy system modeling for multimodal industry data
• Fuzzy system modeling for incomplete industry data
• Evolutionary learning theory in scientific programming
• Multi-object optimization theory in scientific programming
• Sparse optimization theory in scientific programming
• Online scientific programming for complex industry data