Temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques

Abstract

In this paper, we present a new method to deal with temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques. First, the proposed method constructs fuzzy rules from training samples based on the fuzzy C-Means clustering algorithm, where each fuzzy rule corresponds to a cluster and the linguistic terms appearing in the fuzzy rules are represented by triangular fuzzy sets. Then, it performs fuzzy inference based on the multiple fuzzy rules interpolation scheme, where it calculates the weight of each fuzzy rule with respect to the input observation based on the defuzzified values of triangular fuzzy sets. Finally, it uses the weight of each fuzzy rule to calculate the forecasted output. We also apply the proposed method to handle the temperature prediction problem. The experimental result shows that the proposed method gets higher average forecasting accuracy rates than Chen and Hwang's method [7].

IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge

“…We use fuzzy C-means (FCM) clustering algorithm [41] to partition the data points based on their mutual likeliness. During clustering, all data points X j (j = 1, 2, ..., n) are initially assigned a membership value (µ) for all clusters…”

“…We use fuzzy interpolation scheme [49], [41] (FIS) to classify whether a given traffic flow is malicious or benign. FIS uses the sparse fuzzy rule base, consisting of n rules (n = c), obtained from clustering, to detect malicious traffic flows.…”

Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques

“…In [7], Celikyilmaz and Turksen presented a method to predict the automobile fuel consumption and the stock price based on the fuzzy system modeling using an improved fuzzy clustering algorithm. In [8], Chang and Chen proposed a method for temperature prediction based on fuzzy clustering and fuzzy rule interpolation techniques. In [10], Chen and Fang proposed a method to construct fuzzy linear regression models with variable spreads to get high explanatory power and high forecasting accuracy.…”

“…In [37], Zadeh pointed out that to deal with complex real-world phenomena, we require fuzzy logic. In recent years, some fuzzy forecasting methods [5,7,8,[10][11][12]15,16,19,21,22,24,25,29,31,34] have been presented for dealing with forecasting problems. In [5], Cardoso and Gomide presented a method to predict the newspaper demand based on fuzzy clustering and fuzzy rules.…”

Comparative analysis of interpolative and non-interpolative fuzzy rule based machine learning systems applying various numerical optimization methods

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