Development of an adaptive neural-based fuzzy inference system for feeding decision-making assessment in silver perch (Bidyanus bidyanus) culture

The primary objective of this study was to develop an adaptive neural-based fuzzy inference system (ANFIS) for feeding decision-making in aquaculture. Silver perch (Bidyanus bidyanus) were raised under semi-intensive conditions in Taiwan. Because dissolved oxygen (DO) is a key factor that is helpful in detecting the appetite of fish at the initial period of the feeding activity and because the flocking and struggle behaviors of food-searching fish have a transient effect on the measurement of the DO, a simple water-reused rearing tank was prepared to measure the DO to develop a fuzzy logic controller (FLC). In the equivalent ANFIS of the FLC, two linguistic variables were used to describe the food-searching state of the fish population and establish a rule base composed of 15 rules. Furthermore, an alternate hybrid learning approach, which is a fuzzy logic technique based on artificial neural networks, was suggested to quickly model the linguistic variables and evaluate their relative contributions. The results indicated that a decision threshold of 0.17, which was inferred using the fuzzy logic approach, considerably benefits the feeding decision; the high rate of accurate judgments (with an accuracy of 97.89%), which was obtained by the ANFIS model, was close to the actual food searching behaviors of fish. Therefore, the application of the ANFIS model to the feeding decision system in an aquaculture rearing tank has considerable potential for success.