Abstract
In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.
Highlights
In the real world, there are active research studies on prediction in various fields such as weather, energy, communication, control, architecture, and pattern recognition
The adaptive neuro fuzzy inference system (ANFIS) model is applied to various fields as a system that combines artificial neural networks with adaptive and learning ability and fuzzy reasoning similar to the human thinking ability
The fuzzy C-means (FCM) clustering method divides a set of m vectors xi, i = 1, 2, . . . , m into c fuzzy clusters and finds the center within a positive represents the radius of the that the user canclustering set
Summary
There are active research studies on prediction in various fields such as weather, energy, communication, control, architecture, and pattern recognition. Mostafaei [3] predicted and compared the performance of biodiesel fuel cetane numbers using three ANFIS models using grid partitioning, subtractive clustering, and FCM clustering. The neuro-fuzzy inference system generates an if- fuzzy rule through fuzzy inference and optimizes the prediction performance by updating the parameters used in the fuzzy rule by applying the learning ability of the neural network. The Takagi–Sugeno system is efficient in terms of computational ability, is adaptable to generating rules in combination with the optimization method of the artificial neural network and has the advantage of ensuring the continuity of the output space. The backward learning process uses the gradient descent method to optimize the parameters placed in the conditional part of the rule that defines the membership function.
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