Designing of Neural Network Models for Agricultural Forecasting

Autor:	V. Deneshkumar, M. Manikandan, K. Senthamarai Kannan
Rok vydání:	2015
Předmět:	Artificial neural network Computer science business.industry Feed forward neural Machine learning computer.software_genre Visualization Autoregressive integrated moving average Artificial intelligence Agricultural productivity business computer Multi layer Statistical hypothesis testing
Zdroj:	Journal of Statistics and Management Systems. 18:547-559
ISSN:	2169-0014 0972-0510
Popis:	Artificial Neural Network (ANN) provides an attractive alternative tool for researchers for agricultural forecasting. The Multi Layer Feed Forward Neural Net (MLFFNN) is one of the most widely used neural nets. Here, the MLFFNN architecture is examined and compared with time series models such as Autoregressive Integrated Moving Average (ARIMA) model for prediction of agricultural production. Both models were compared using visualization technique and statistical tests and the results were illustrated numerically and graphically.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f313372a4aac732fa39cc0301960d5b8 https://doi.org/10.1080/09720510.2015.1040237 Zobrazit plný text záznamu