The Experts below are selected from a list of 36447 Experts worldwide ranked by ideXlab platform

Lufeng Yu - One of the best experts on this subject based on the ideXlab platform.

  • a 24 h forecast of oxidant concentration in tokyo using neural network and fuzzy learning approach
    Clean-soil Air Water, 2013
    Co-Authors: Keisuke Hanaki, Hanchang Su, Lufeng Yu
    Abstract:

    In this study, several types of adaptive network-based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including Linguistic Expression of MFs and if-then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.

  • A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach
    Clean-soil Air Water, 2013
    Co-Authors: Keisuke Hanaki, Hanchang Su, Lufeng Yu
    Abstract:

    In this study, several types of adaptive network-based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including Linguistic Expression of MFs and if-then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.

Kazuhisa Seta - One of the best experts on this subject based on the ideXlab platform.

  • improved method for Linguistic Expression of time series with global trend and local features
    IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Motohide Umano, Mitsuhiro Okamura, Kazuhisa Seta
    Abstract:

    We have various kinds of time series such as stock prices. We understand them via their Linguistic Expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a Linguistic Expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of Multimodal Summarization for Trend Information (MuST).

  • FUZZ-IEEE - Improved method for Linguistic Expression of time series with global trend and local features
    2009 IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Motohide Umano, Mitsuhiro Okamura, Kazuhisa Seta
    Abstract:

    We have various kinds of time series such as stock prices. We understand them via their Linguistic Expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a Linguistic Expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of Multimodal Summarization for Trend Information (MuST).

Keisuke Hanaki - One of the best experts on this subject based on the ideXlab platform.

  • a 24 h forecast of oxidant concentration in tokyo using neural network and fuzzy learning approach
    Clean-soil Air Water, 2013
    Co-Authors: Keisuke Hanaki, Hanchang Su, Lufeng Yu
    Abstract:

    In this study, several types of adaptive network-based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including Linguistic Expression of MFs and if-then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.

  • A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach
    Clean-soil Air Water, 2013
    Co-Authors: Keisuke Hanaki, Hanchang Su, Lufeng Yu
    Abstract:

    In this study, several types of adaptive network-based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including Linguistic Expression of MFs and if-then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.

Motohide Umano - One of the best experts on this subject based on the ideXlab platform.

  • improved method for Linguistic Expression of time series with global trend and local features
    IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Motohide Umano, Mitsuhiro Okamura, Kazuhisa Seta
    Abstract:

    We have various kinds of time series such as stock prices. We understand them via their Linguistic Expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a Linguistic Expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of Multimodal Summarization for Trend Information (MuST).

  • FUZZ-IEEE - Improved method for Linguistic Expression of time series with global trend and local features
    2009 IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Motohide Umano, Mitsuhiro Okamura, Kazuhisa Seta
    Abstract:

    We have various kinds of time series such as stock prices. We understand them via their Linguistic Expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a Linguistic Expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of Multimodal Summarization for Trend Information (MuST).

Hanchang Su - One of the best experts on this subject based on the ideXlab platform.

  • a 24 h forecast of oxidant concentration in tokyo using neural network and fuzzy learning approach
    Clean-soil Air Water, 2013
    Co-Authors: Keisuke Hanaki, Hanchang Su, Lufeng Yu
    Abstract:

    In this study, several types of adaptive network-based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including Linguistic Expression of MFs and if-then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.

  • A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach
    Clean-soil Air Water, 2013
    Co-Authors: Keisuke Hanaki, Hanchang Su, Lufeng Yu
    Abstract:

    In this study, several types of adaptive network-based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including Linguistic Expression of MFs and if-then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.