A neuro-fuzzy mixing control model for the cooking process of coconut sugar

• Main Authors: Aquino, Aaron U., Bautista, Mary Grace Ann C., Baldovino, Renann G., Calilung, Edwin J., Sybingco, Edwin, Dadios, Elmer Jose P.
• Format: text
• Published: Animo Repository 2017
• Subjects: Sugar—Mixing; Process control; Adaptive control systems; Fuzzy logic; Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2772
• Institution: De La Salle University

This study presents a neuro-fuzzy system used in developing an appropriate model for the mixing control of the coconut sugar cooking process. The developed model is trained and tested using actual data and process gathered from cooking coconut sap run in several trials. Adaptive neuro-fuzzy inference system (ANFIS) was the primary tool used to model the control cooking process. Grid partition, subtractive clustering and fuzzy c-means clustering were used in the fuzzification of the training data. Then, the neural network generates the fuzzy rules for the model, which are evaluated to measure the performance of the model. Moreover, experimental results show the detailed comparison of the performance of each fuzzy model. Among the 3 training models used, the fuzzy c-means clustering provided the best performance with only 3 fuzzy rules extracted with an accuracy of 95.4% during testing.