Improving Wine Quality Forecasts Using Dynamic Integral Neural Networks and Optimized Against Interference
Keywords:
Dynamic Integral Neural Network, Wine quality prediction, Anti-interference optimization, Robust prediction models, Machine learningAbstract
As a very important part of the wine sector, wine quality prediction covers vital aspects of the industry that have to do with effective production possibilities and customer satisfaction. The complexity of determining wine quality is enhanced by increasing numbers of varieties of grapes, methods of fermentation, and geo graphical factors, which make the need for enhanced prediction models. The main conventional techniques largely involve the use of sensory assessments, which are notorious for their bias and inaccuracy while on the other hand; machine- learn ing techniques have turned out to be very reliable. In this paper, a Differential evolution algorithm with exponential crossover is incorporated into a Dynamic Integral Neural Network (DINN) optimized for anti-interference as a new wine quality prediction model. Substituting relevant integral components, the proposed model cancels out noise and interference, which is always witnessed in real-life data, thus boosting the capability of the model to learn with fluctuating and im precise data for better and uniform outcomes. Stringent testing proves that the DINN model works better than other neural network structures under considera tion by showing such advantages as increased dependability, higher speeds of calculation, and better resilience to unusual data values. Comparison with the classic type shows that the DINN gives a better performance in noisy environ ments and overall predictive accuracy. Its usage for the wine industry seems very promising as the main issue in this area is the presence of outliers, which distort the data and result from the variability of the measurement techniques and envi ronmental conditions. The results support DINNs’ applicability for revolutionizing wine quality evaluation and pave the way for an efficient DINN implementation in various real-world settings; future works could include the inclusion of IoT sys tems for real-time wine quality evaluation, and testing DINNs on seemingly limit less datasets of different winemaking settings.
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