Specific K-mean clustering-based perceptron for dengue prediction. (2017) - Nguyen, Hoang Long; Duong, Trong Hai; Nguyen, Cuong Phan; Nguyen, Duc Cuong; Chiem, Thach Phat; Nguyen, Manh Hung; Nguyen, Thi Nhu Mai; Nguyen, Hung Vi
Traditional neural networks come up with drawback relating to choosing the number of nodes in each layer. This paper proposes a novel adaptive network fuzzy inference system (ANFIS) to overcome the aforementioned problem. In particular, we use incremental k-mean to pre-identify the number of nodes in the adaptive network. Each node includes a set of samples in a training set. For each sample, we identify a fuzzy value of the particular sample data belonging to each node in the network. The learning perceptron algorithm also investigates to adjust weights by learning from real output data. In this study, the novel ANFIS model is employed to the dengue prediction application as well as evaluates performance execution by a real dataset of dengue disease in Tien Giang, Vietnam. The result shows that our proposed model of ANFIS gets better accuracy in comparison with linear regression, multiple linear regression, time series and neural network.