In this paper, the continuous time recurrent neural network is proposed to solve the functional minimization problem, which is often involved in estimation and control.
针对信息科学和控制理论中经常涉及的一类泛函极值问题，提出基于连续回归神经网络的求解方法。
2
The method of taking continuous qualified product counts as the control objects and the kernel technology of neural network for realizing this method are introduced.
介绍了以连续合格品数为控制对象的方法以及实现该方法的神经网络核心技术。
3
For reinforcement learning control in continuous Spaces, a Q-learning method based on a self-organizing fuzzy RBF (radial basis function) network is proposed.