Abstract
We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, multiple ANFISs were designed in an incremental tree structure without using a single ANFIS. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset. The prediction experiment verified that the proposed CFCM-clustering-based ANFIS shows better prediction efficiency than the current grid-based and clustering-based ANFISs in the form of an incremental tree.
Highlights
In optimizing the nonlinear system model, the neuro-fuzzy system has exhibited better performance than the model based on the existing linear system [1,2,3,4,5,6,7,8,9,10,11]
To prevent over-generation of fuzzy rules due to large-scale databases and to generate meaningful rules, we propose a context-based fuzzy C-means (CFCM)-adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure rather than a single type of CFCM-ANFIS
To evaluate the predicted performance of ANFIS with an incremental tree structure based on CFCM clustering methods described in Section 3, experiments were conducted to predict computer performance using the computer hardware dataset
Summary
In optimizing the nonlinear system model, the neuro-fuzzy system has exhibited better performance than the model based on the existing linear system [1,2,3,4,5,6,7,8,9,10,11]. In the grid-based rule generation method, when the dimension of the input increases or the number of membership functions (MFs) increases, the rule of the neuro-fuzzy system model increases exponentially. C-means (CFCM) clustering-based rule generation approach instead of a general clustering-based rule generation methodology that takes into account the patterns of the input space as well as the output space and ANFIS in the form of an incremental tree structure rather than a single structure. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset [32].
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