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Information Journal Paper

Title

SINGLE HIDDEN LAYER ARTIFICIAL NEURAL NETWORK MODELS VERSUS MULTIPLE LINEAR REGRESSION MODEL IN FORECASTING THE TIME SERIES OF TOTAL OZONE

Pages

  141-149

Abstract

 Present paper endeavors to develop predictive ARTIFICIAL NEURAL NETWORK model for FORECASTing the mean monthly TOTAL OZONE concentration over AROSA, Switzerland. Single hidden layer neural network models with variable number of nodes have been developed and their performances have been evaluated using the method of least squares and error estimation. Their performances have been compared with MULTIPLE LINEAR REGRESSION model. Ultimately, SINGLE-HIDDEN-LAYER model with 8 hidden nodes have been identified as the best predictive model.

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  • Cite

    APA: Copy

    BANDYOPADHYAY, G., & CHATTOPADHYAY, S.. (2007). SINGLE HIDDEN LAYER ARTIFICIAL NEURAL NETWORK MODELS VERSUS MULTIPLE LINEAR REGRESSION MODEL IN FORECASTING THE TIME SERIES OF TOTAL OZONE. INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (IJEST), 4(1 (13)), 141-149. SID. https://sid.ir/paper/285007/en

    Vancouver: Copy

    BANDYOPADHYAY G., CHATTOPADHYAY S.. SINGLE HIDDEN LAYER ARTIFICIAL NEURAL NETWORK MODELS VERSUS MULTIPLE LINEAR REGRESSION MODEL IN FORECASTING THE TIME SERIES OF TOTAL OZONE. INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (IJEST)[Internet]. 2007;4(1 (13)):141-149. Available from: https://sid.ir/paper/285007/en

    IEEE: Copy

    G. BANDYOPADHYAY, and S. CHATTOPADHYAY, “SINGLE HIDDEN LAYER ARTIFICIAL NEURAL NETWORK MODELS VERSUS MULTIPLE LINEAR REGRESSION MODEL IN FORECASTING THE TIME SERIES OF TOTAL OZONE,” INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (IJEST), vol. 4, no. 1 (13), pp. 141–149, 2007, [Online]. Available: https://sid.ir/paper/285007/en

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