Multiple-Input Multiple-Output (MIMO) digital communications standards usually acquire Channel State Information (CSI) by means of supervised algorithms, which implies loss of performance since pilot symbols do not convey information. We propose obtaining this CSI by using semi-blind techniques, which combine both supervised and unsupervised (blind) methods. The key idea consists in introducing a decision criterion to determine when the channel suffered a significant change. In such a case, transmission of pilot symbols is required. The use of this criterion also allows us to determine the time instants in which CSI has to be sent to the transmitter from the receiver through a low-cost feedback channel.
One difficulty for quaternion neural networks (QNNs) is that quaternion nonlinear activation functions are usually non-analytic and thus quaternion derivatives cannot be used. In this paper, we derive the quaternion gradient descent, approximated quaternion Gauss-Newton and quaternion Levenberg-Marquardt algorithms for feedforward QNNs based on the GHR calculus, which is suitable for analytic and non-analytic quaternion functions. Meanwhile, we solve a widely linear quaternion least squares problem in the derivation of quaternion Gauss-Newton algorithm, which is more general than the usual least squares probŹlem. A rigorous analysis of the convergence of the proposed algorithms is provided. Simulations on the prediction of benchmark signals support the approach.
This article presents an alternative approach useful for medical practitioners who wish to detect malaria and accurately identify the level of severity. Malaria classi?ers are usually based on feed forward neural networks. In this study, the proposed classiffier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, confusion matrix, mean square error, determinant coefficient, and reliability. The effectiveness of the classiffier is compared to a support vector machine and multiple regression models. The results of the comparative analysis demonstrate a superior performance level of the Jordan-Elman neural network model. Further comparison of the classier with previous literature indicates performance improvement over existing results. The Jordan-Elman neural networks classiffier can assist medical practitioners in the fast detection of malaria and determining its severity, especially in tropical and subtropical regions where cases of malaria are prevalent