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Convolutional neural networks (CNNs) are widely used to categorize images. Successful training of a CNN requires rapid convergence of its weights, which increases the efficiency of training. In this paper, a CNN-RDM model, based on CNN and representational dissimilarity matrix (RDM), is proposed. In the proposed model, a loss function is defined in the RDM whose output is a minimized dissimilarity matrix. Therefore, by combining this minimized matrix of RDM loss function with CNN cross-entropy, the final output is minimized. The inputs of CNN-RDM model are pixels with low-level features, and the output is a set of features. The CNN-RDM model can actually change the structure of the neural network inspired by the visual pathway. The performance of our model is evaluated using 50 batches, including 10 image classes, each containing 5 samples on the data sets Cifar10, Cifar100, and Coco. Evaluation results demonstrate that our CNN-RDM model has achieved an accuracy of 60% in image classification, which is superior to 51% accuracy of the obtained model.

In this study, a direct torque and flux control is described for a six-phase asymmetrical speed and voltage sensorless induction machine (IM) drive, based on non-linear backstepping control approach. First, the decoupled torque and flux controllers are developed based on Lyapunov theory, using the machine two axis equations in the stationary reference frame. In this control scheme, the actual stator voltages are determined from dc-link voltage using the switching pattern of the space vector pulse-width modulation inverter. Then, for a given motor load torque and rotor speed, a so-called fast search method is used to maximise the motor efficiency. According to this method, the rotor reference flux is decreased in the small steps, until the average of real input power to the motor reaches to a minimum value. In addition, a model reference adaptive system-based observer is employed for online estimating of the rotor speed. Finally, the feasibility of the proposed control scheme is verified by simulation and experimental results.