Pattern recognition using neural network time series - D, Nagarajan; G, Kavitha (International Journal of Engineering and Technology, 2018)
Pattern recognition mainly concentrate on identification of designs to identify the characterized by prefixed principle on a data. Classification of IRIS dataset has been taken to examine the petal and sepal size of the IRIS flower and to predict analyzing to which pattern the class of IRIS flower really belongs to. In this paper, the model has been trained with Neural Network time series analysis to recognize the pattern of IRIS flower. Pattern recognition is a field of cognate such as image processing and neural network .Pattern recognition mainly concentrate on identification of designs to identify the characterized by prefixed principle on a data. Classification of IRIS dataset has been taken to examine the petal and sepal size of the IRIS flower and to predict analyzing to which pattern the class of IRIS flower really belongs to. In this paper, the model has been trained with Neural Network time series analysis to recognize the pattern of IRIS flower. The paper applies neural networks for forecasting. The learning rule in neural network modifies the parameters for a given input to give a desired output. The proposed research work identifies patterns using supervised neural network training algorithm to accurately predict the behavioral pattern in IRIS flower species.