Performance evaluation of optimized and adaptive neuro fuzzy inference system for predictive modeling in agriculture. (September 2020)

• Record Type: Journal Article
• Authors: Remya, S; Sasikala, R
• Is Part Of: Computers & electrical engineering. Volume 86(2020)
• Publication Date: 2020-09
• Subjects: Predictive modeling -- Optimization -- Adam -- Agriculture -- Neuro-fuzzy -- Learning rate -- Deep learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106718
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Improve speed of learning process. Decreases the computational time. To reduce the complexity of a model and makes it simple and easier to interpret. To improve the accuracy of a model and reduce over fitting. Abstract: The Neural Network has a significant impact in developing predictive models in a wide range of applications. In this paper, a neuro-fuzzy prediction model is developed depending on improving the performance of the traditional artificial neural networks using Adaptive Momentum Optimizer. This optimizer simulates the behavior of the International Trade Analysis in the agriculture industry, and this method is used to determine the optimal parameters of artificial neural networks. The proposed model is compared with the existing models such as Support Vector Machine, Random Forest, Decision Tree and traditional Artificial Neural Network models. To examine the forecasting performance of the proposed approach, agriculture datasets is used. The performance of the models was assessed using different performance evaluation criteria and the empirical results show that the back propagation neural network with Adam optimizer attains favorable prediction accuracy of 96.78%, and a better convergence rate. Compared to other benchmark algorithms, the proposed algorithm performs better, and the result validates the effectiveness of the back propagation with Adam optimizer for Natural Language Processing. Graphical abstract: Image, graphical abstract