Iranian Research Organization for Science and Technology (IROST) Innovative Food Technologies 2783-350X 6 4 2019 07 23 Biological Properties Classification of Pear Fruit in Dynamic and Static Loading using Artificial Neural Network Biological Properties Classification of Pear Fruit in Dynamic and Static Loading using Artificial Neural Network 507 520 881 10.22104/jift.2018.3185.1764 FA Mohsen Azadbakht Associate Professor,Department of Bio-System Mechanical Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran Mohammad Vahedi Torshizi MSc. Student, Department of Bio-system Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran 0000-0003-3648-1515 Ali Asghari Assistant Professor,Department of Bio-System Mechanical Engineering,Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. Journal Article 2018 10 04 Artificial Neural Networks (ANNs) are powerful modeling techniques that work in brief with arrays of neurons in memory and biological learning. In this research, the classification of dynamic and quasi-static loading type (broad and thin edge) was investigated using input data of phenol, antioxidant, vitamin C content and stiffness with artificial neural network. In this experiment for the classification of two Radial basic function and Multilayer perceptron networks were used with two hyperbolic tangent and sigmoid activation functions in one layer. According to the obtained results, the best value for R and Percent Correct for dynamic loading was (Percent Correct = 100 - R = 9999997), loading the thin edge (Percent Correct = 100 - R = 9999993) and loading the wide edge (Percent Correct = 100 - R = 9999992), which was created in the RBF network with a sigmoid function activation and 8 neurons in the one hidden layer. Also, the most accurate data found for the dynamic loading type, the wide edge and the thin edge was observed in the networks created for the RBF network, and this network has been able to 100% accurately classify the data rate for all loads. In sum, the neural network with the input of general data has the desirable capability in the stacking of dynamic loading and quasi-static data. Artificial Neural Networks (ANNs) are powerful modeling techniques that work in brief with arrays of neurons in memory and biological learning. In this research, the classification of dynamic and quasi-static loading type (broad and thin edge) was investigated using input data of phenol, antioxidant, vitamin C content and stiffness with artificial neural network. In this experiment for the classification of two Radial basic function and Multilayer perceptron networks were used with two hyperbolic tangent and sigmoid activation functions in one layer. According to the obtained results, the best value for R and Percent Correct for dynamic loading was (Percent Correct = 100 - R = 9999997), loading the thin edge (Percent Correct = 100 - R = 9999993) and loading the wide edge (Percent Correct = 100 - R = 9999992), which was created in the RBF network with a sigmoid function activation and 8 neurons in the one hidden layer. Also, the most accurate data found for the dynamic loading type, the wide edge and the thin edge was observed in the networks created for the RBF network, and this network has been able to 100% accurately classify the data rate for all loads. In sum, the neural network with the input of general data has the desirable capability in the stacking of dynamic loading and quasi-static data. https://jift.irost.ir/article_881_84d2e45f8d644ea703e42dfd56d03d46.pdf