## 2 The Development Stages of Grain-Drying Technology
### The Current State of Research on Grain Dryer Control Systems Abroad
#### 3.1.3 Artificial Neural Network Control  
Artificial neural networks (ANNs) learn and predict by simulating the structure of the neural network of the human brain, effectively dealing with the complex nonlinear problems in the grain-drying process. The ANN not only learns the operating characteristics of the dryer but also uses the acquired knowledge to assess new situations and adjust control strategies accordingly to adapt to changes. This type of control has good and robust control accuracy [61]. Table 4 provides a comprehensive introduction to the research progress in artificial neural network control technology, including its applications in various fields, historical origins, and future development directions in conjunction with AI. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Time** & **Development Status** & **References** \\ \hline
1940s–1950s & Psychologists [PERSON] and [PERSON] proposed the first artificial neuron model, which became the foundation for the development of artificial neural networks. & [48; 62; 63] \\ \hline
1960s–1970s & Computer scientist [PERSON] proposed the perceptron model, marking the beginning of practical applications for artificial neural networks. & [48; 64; 65] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The development timeline of artificial neural network control. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Time** & **Development Status** & **References** \\ \hline
1965s & Japanese scholar [PERSON] first proposed the theory of fuzzy sets, laying the theoretical foundation for fuzzy logic control. & [48; 49; 50] \\ \hline
1972s & Professor [PERSON] from the University of California, Berkeley, first proposed the concept of fuzzy logic control systems, pioneering the application of fuzzy logic logic in the field of control. & [48; 51; 52] \\ \hline
1980s & Fuzzy logic control technology has been widely applied and studied, achieving particularly significant progress in the fields of industrial control, intelligent systems, and automation. & [53; 54; 55] \\ \hline
1990s–hitherto & Fuzzy logic control has been widely applied in fields such as robotic control, traffic control, medical diagnosis, and financial analysis, continuously achieving new breakthroughs and advancements. & [56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88;  
[PERSON] et al. (2024), inspired by the functional behavior of the human brain's biological nervous system, summarized the application of artificial neural network methods in modeling various dehydration methods and outlined the opportunities and limitations of artificial neural network technology in the simulation, optimization, and control of the drying process to guide future R&D in this field. Artificial neural networks have been used in drying technology for modeling, optimizing heat and mass transfer, and improving the physicochemical characteristics of dried products. [PERSON] et al. (2020), to explore the effects of different drying conditions on the quality and thermodynamic properties of almonds, conducted experiments with a convective dryer. Ultrasonic pretreatment was carried out at air temperatures of 40, 50, 60, and 70 \({}^{\circ}\)C, an air velocity of 1 m/s, and ultrasonic pretreatment durations of 0 min (control sample), 10, 20, and 40 min. The drying kinetics of almonds were estimated using 15 mathematical models. Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) were used to study the effects of different drying conditions on the quality, drying, and thermodynamic properties of almonds in a convective dryer with ultrasonic pretreatment. The results showed that the ANFIS model has a higher ability to evaluate output compared to the ANNs method.