A recallingenhanced recurrent neural network: conjugate gradient learning algorithm and its convergence analysis Abstract   Elman network is a classical recurrent neural network with an internal delay feedback. In this paper, we propose a recallingenhanced recurrent neural network (RERNN) which has a selective memory property. In addition, an improved conjugate algorithm with generalized Armijo search technique that speeds up the convergence rate is used to train the RERNN model. Further enhancement performance is achieved with adaptive learning coefficients. Finally, we prove weak and strong convergence of the presented algorithm. In other words, as the number of training steps increases, the following has been established for RERNN: (1) the gradient norm of the error function with respect to the weight vectors converges to zero, (2) the weight sequence approaches a fixed optimal point. We have carried out a number of simulations to illustrate and verify the theoretical results that demonstrate the efficiency of the proposed algorithm.

Feature extraction and Classification of Learners using MultiContext Recurrent Neural Networks This study presents about a new procedure for extraction and classification of learners in a class using the neural networks. It is necessary to provide learning support corresponding to the understanding degree of each learner to improve learning process efficiency. For this purpose, this study develops a procedure to predict the achievement level of learners at the end of the class and classify them. A MultiContext Recurrent Neural Network (MCRNN) is used for predicting achievement level and classifying learners. By providing additional education for the learners who are classified as a low degree by the proposed method, it is expected to be able to take countermeasures for not becoming dropout in early stage. In this study, numerical experiments are executed to verify the usefulness of the proposed method. To gather enough number of learnersu0027 data, this study generates the learnersu0027 growth process data that used as training and test data of MCRNN. The experimental result indicates that the proposed method succeeded in classifying learners into three groups based on the understanding degree at the end of a class.