This paper proposes an efficient on-line learning method for radial basis function (RBF) neural networks.
本文提出了一种径向基函数神经网络的有效在线学习方法。
The simulation results present that the proposed control strategy is valid and the on-line learning time is abbreviated as well.
仿真结果表明该控制策略的可行性，并且可以有效缩短在线学习时间。
The property of clustering learning of the DRNN makes it very suitable for real-time speech recognition with on-line learning ability.
DRNN聚类学习的性能使得它非常适用于与在线学习方式相结合的实时语音识别系统。
Reinforcement learning based on Markov decision process is a way of on-line learning, which can be applied to single agent environment.
基于马尔科夫过程的强化学习作为一种在线学习方式，能够很好地应用于单智能体环境中。
In This paper, an on-line learning controller based on CMAC with Gauss Basis Function Neural Network is presented, and its algorithm is discussed.
本文提出了基于高斯基函数CMAC在线学习的控制方案，并给出了相应的学习算法。
First, a study of a sliding mode controller under on off training is made and then the on line learning algorithm using a gradient decent method is designed.
首先研究了离线训练的滑模控制器，然后，给出了利用梯度下降法的在线训练方法。
The lurkers' behavior has influenced the factual interactive level of teaching and learning, and even more it has affected the achievement of on-line learning.
这种观望行为影响了实际的教学交互水平，更影响了学习者的在线学习效果。
An active noise self-tuning model predictive control approach is derived. An on-line learning algorithm is proposed for adjusting the uncertain model parameters.
将预测控制方法应用到有源噪声控制领域，给出了一种参数在线自适应算法，该算法的收敛速度不受次级声路径响应的影响。
This learning method rests on the creative on-line studying environment concerning multi-media net resources, on-line learning community, and net technique platform.
这种学习方式建立在多媒体网络学习资源、网上学习社区及网络技术平台等构成的全新的网络学习环境基础上。
Controller is designed based on VHDL, the research keys are the implementation of the on-line learning algorithm of CMAC and the closed-loop simulation test of the controller.
基于VHDL设计该控制器，重点在于CMAC的在线学习算法实现和控制器模块的闭环仿真测试。
A new type of adaptive PID controller using diagonal recurrent neural network (DRNN) is presented. An on-line learning algorithm based on PID parameter self-tuning method is given.
提出了一种基于对角回归神经网络的PID控制器结构，给出了PID参数在线自整定的学习控制算法。
The MNN can be used for on -line learning of the plant's dynamic characteristics and provide a kind of adaptive control strategy which can improve the whole control system's performance.
该MNN网络能用于在线学习对象的动态特性，从而提供一种能提高整个控制系统性能的自适应控制实现策略。
The MNN can be used for on -line learning of the plant's dynamic characteristics and provide a kind of adaptive control strategy which can improve the whole control system's performance.
该MNN网络能用于在线学习对象的动态特性，从而提供一种能提高整个控制系统性能的自适应控制实现策略。
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