Wheat seed classification using neural network pattern recognizer

Madhavan, J; Salim, Mohammed; Durairaj, Udayakumar; Kotteeswaran, R.

doi:10.1016/j.matpr.2021.03.226

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…TN (True Negative) represents the number of samples whose true value was negative and predicted value was positive, where FN and FP are the first and second types of error. Theoretically, the larger the TP and TN, the more accurate the model, and the smaller the FP and FN, the better the performance [25,26]. and 30% for verification; they were randomly rotated −90° to 90° and also randomly zoomed in and out by 1 to 2 times.…”

Section: Machine Learning and Model Performance Indexmentioning

confidence: 99%

Identification and Machine Learning Prediction of Nonlinear Behavior in a Robotic Arm System

Wang

Zhu

2021

Symmetry

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In this study, the subject of investigation was the dynamic double pendulum crank mechanism used in a robotic arm. The arm is driven by a DC motor though the crank system and connected to a fixed side with a mount that includes a single spring and damping. Robotic arms are now widely used in industry, and the requirements for accuracy are stringent. There are many factors that can cause the induction of nonlinear or asymmetric behavior and even excite chaotic motion. In this study, bifurcation diagrams were used to analyze the dynamic response, including stable symmetric orbits and periodic and chaotic motions of the system under different damping and stiffness parameters. Behavior under different parameters was analyzed and verified by phase portraits, the maximum Lyapunov exponent, and Poincaré mapping. Firstly, to distinguish instability in the system, phase portraits and Poincaré maps were used for the identification of individual images, and the maximum Lyapunov exponents were used for prediction. GoogLeNet and ResNet-50 were used for image identification, and the results were compared using a convolutional neural network (CNN). This widens the convolutional layer and expands pooling to reduce network training time and thickening of the image; this deepens the network and strengthens performance. Secondly, the maximum Lyapunov exponent was used as the key index for the indication of chaos. Gaussian process regression (GPR) and the back propagation neural network (BPNN) were used with different amounts of data to quickly predict the maximum Lyapunov exponent under different parameters. The main finding of this study was that chaotic behavior occurs in the robotic arm system and can be more efficiently identified by ResNet-50 than by GoogLeNet; this was especially true for Poincaré map diagnosis. The results of GPR and BPNN model training on the three types of data show that GPR had a smaller error value, and the GPR-21 × 21 model was similar to the BPNN-51 × 51 model in terms of error and determination coefficient, showing that GPR prediction was better than that of BPNN. The results of this study allow the formation of a highly accurate prediction and identification model system for nonlinear and chaotic motion in robotic arms.

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Section: Machine Learning and Model Performance Indexmentioning

confidence: 99%