To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real tim...To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15×15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.展开更多
教育技术的发展包含了一系列不同的范式。随着大数据的崛起和数据密集科学的发展,学习分析学(LA,Learning Analytics)和教育数据挖掘(EDM,Educational Data Mining)成为大数据在教育领域的具体应用,基于数据的教学干预应用程式已出现并...教育技术的发展包含了一系列不同的范式。随着大数据的崛起和数据密集科学的发展,学习分析学(LA,Learning Analytics)和教育数据挖掘(EDM,Educational Data Mining)成为大数据在教育领域的具体应用,基于数据的教学干预应用程式已出现并在实际教学中使用(如Signals,Moodog等)。文章探讨了科学范式,大数据在教育技术领域的应用,以及不同的教育技术范式,提出由于能够更好地贯彻"以学习者为中心"的教育理念,个性化自适应学习系统将成为以大数据为基础的新的教育技术范式。展开更多
基金Project(50276005) supported by the National Natural Science Foundation of China Projects (2006CB705400, 2003CB716206) supported by National Basic Research Program of China
文摘To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15×15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.
文摘教育技术的发展包含了一系列不同的范式。随着大数据的崛起和数据密集科学的发展,学习分析学(LA,Learning Analytics)和教育数据挖掘(EDM,Educational Data Mining)成为大数据在教育领域的具体应用,基于数据的教学干预应用程式已出现并在实际教学中使用(如Signals,Moodog等)。文章探讨了科学范式,大数据在教育技术领域的应用,以及不同的教育技术范式,提出由于能够更好地贯彻"以学习者为中心"的教育理念,个性化自适应学习系统将成为以大数据为基础的新的教育技术范式。
