An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

Abstract

The adaptive neuro-fuzzy inference system (ANFIS) is employed in a vast range of applications because of its smoothness (by Fuzzy Control (FC)) and adaptability (by Neural Network (NN)). Although ANFIS is better in nonlinear optimization, two major loopholes need to be addressed thoroughly. They are the curse of dimensionality and computational complexity. To overcome these complications, a novel usage of the ANFIS model is proposed as Cascaded ANFIS in this paper. As the primary source of this algorithm, a general two-input one-output ANFIS algorithm is used. The novel algorithm has two main modules called pair selection and training model. Pair selection is responsible for selecting the best match for the inputs, while the training module generates the output. The cascaded behavior of the novel algorithm generates additional iterations to advance the best solution. Even though the number of parameters that need to be adjusted is increasing at each additional iteration, the complexity of the algorithm may stay stable. Two-hybrid state-of-the-art algorithms are used to compare the performance of the novel algorithm, namely, Particle Swarm Optimization-based ANFIS (ANFIS-PSO) and Genetic Algorithm-based ANFIS (ANFIS-GA). Furthermore, individual performance is presented for seven publicly recognized datasets. The results have demonstrated that, for some datasets, the root means square error (RMSE) can be minimum as 0.0001.

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Recommended citation: Rathnayake, Namal, Tuan Linh Dang, and Yukinobu Hoshino. “A novel optimization algorithm: cascaded adaptive neuro-fuzzy inference system.” International Journal of Fuzzy Systems 23.7 (2021): 1955-1971.