Processing Online Massive Measuring Databases via Data-Uncertainty Quantifying Mechanism to Synthesize ANFIS


Adaptive neuro-fuzzy inference systems (ANFISs) deriving from big data bring us the perspective in many fields. However, online performing both processing noisy and massive databases (NMDs) and training ANFISs is a challenge. Inspired by this aim, we propose a strategy with two phases, offline and online. The offline discovers an optimal data screening threshold (ODST) which is interpreted as an index to measure the uncertainty of the data in a data cluster. A new algorithm named A-ODST is proposed to estimate the ODST. Using the kernel fuzzy C-means clustering technique, a new filter named FbMU for blurring the ODST-based measured data-uncertainty is presented. An improved algorithm named NMD-ANFIS is presented to build the ANFIS from the NMD filtered by the FbMU. Based on the three main contributions of this paper to be the A-ODST, FbMU, and NMD-ANFIS, processing NMD and training ANFIS can be performed synchronously in the online phase. The combination of the solution to optimize the cluster data space via the NMD-ANFIS to simplify ANFIS’s structure and the filtering strategy of the FbMU for removing all the data points belonging to the data clusters with the highest uncertainty allows both filtering noise and reducing the size of the database to improve the calculating cost. Surveys from two experimental systems were carried out to verify these aspects. The compared results showed that the predicting error and the calculating time of the ANFISs built by the proposed method were better than that from the other surveyed methods.

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Average value of MAE3ms

\({\mathbf{a}}\) :

Hyper-plane index vector

C :

Number of data clusters

\(d_{k}\) :

Relative standard deviation factor of \(\varGamma^{k}\)

\([d]_{\text{op}}\) :

Optimal data screening threshold

E :

Root mean square error


Desired E

\(F\) :

Damping force

\(I(t)\) :

Current intensity at time t

MAE3ms :

Mean absolute error of F in three measurements


Maximum value of MAE3ms in a cycle

\(P\) :

Number of data samples in the considered data set

\(p_{k}\) :

The number of input data points in \(\varGamma^{(k)}\)

\(\bar{p}\) :

The mean number of data points per cluster

\(t_{k}\) :

Number of data samples in \(w_{k} (.)\)

\(V(t)\) :

Relative piston–cylinder velocity

\({\mathbf{x}}(t)\) :

State variable vector

\(({\bar{\mathbf{x}}}_{i} ,y_{i} ),\;i = 1 \ldots P\) :

The ith input–output data sample

\({\bar{\mathbf{x}}}_{i}^{0}\) :

The ith cluster centroid

\({\bar{\mathbf{x}}}_{i}\) :

The ith input data point

\(y_{i}\) :

The ith output data point

\(\varGamma^{k}\) :

The kth data cluster

\(\mu_{ij}\) :

Membership degree of the jth sample belonging to the ith cluster

\(w_{k} (.)\) :

The kth hyper-plane


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The authors acknowledge the valuable comments of the reviewers in improving the quality of the paper. This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.01-2019.328.

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Correspondence to Sy Dzung Nguyen or Seung-Bok Choi.

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Nguyen, S.D., Choi, S. Processing Online Massive Measuring Databases via Data-Uncertainty Quantifying Mechanism to Synthesize ANFIS. Int. J. Fuzzy Syst. 22, 1679–1693 (2020).

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  • Optimal data screening threshold
  • Kernel fuzzy C-means clustering
  • Impulse noise filtering
  • Applications of ANFIS