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DynamicPowerQualityTestingTechnology
- 本文首先介绍了有源电力滤波器的基本结构和工作原理，并对一些常规的谐波电流检测方法的优缺点进行了比较。其次，针对传统的谐波电流检测方法的缺陷，提出将神经网络与基于噪声抵消原理的自适应谐波检测相结合，利用径向基函数运算量小、收敛快、无局部极小值等优点，构造了一种基于径向基函数神经网络的谐波电流检测方法，仿真结果表明该检测方法具有很好的动态响应及畸变电流检测精度。最后，设计了一套实验系统，对本文所采用的系统方案进行了实验验证。仿真表明，本文所采用的有源电力滤波器检测系统方案切实可行，能够较好地实现动态
erweifeixianxing
- 在程序设计和调试过程中①只要在程序中增加类似m或n的循环语句，便可用神经网对三维或三维以上的非线形函数训练，也可以用在复杂系统二个以上独立变量、任意维非线性动态参数的辨识-In program design and debugging process ① similar increase in the program as long as m or n of the loop, you can use the neural network over a three-dimensional or
sonno
- TGSOM一种用于数据聚类的动态自组织映射神经网络,一篇不错的论文-Dynamic TGSOM for data clustering self-organizing map neural network, a good thesis
CONTROLLER-PARAMETERS-TUNING-USING-GENETIC-ALGORI
- The paper deals with a controller design for the nonlinear processes using genetic algorithm and neural model. The aim was to improve the control performance using genetic algorithm for optimal PID controller tuning. The plant model has been id
flight-envelope-protection
- 飞行边界保护系统设计文献，包括神经网络，动态逆，预测控制-The literature about protection sysytem of UAV.Including neural network,Dynamic inverse and MPC.
Adaptive-Dynamic-Surface-Control
- Neural Network-Based Adaptive Dynamic Surface Control for Permanent Magnet Synchronous Motors
ADP-Hawaii-FL-plenary-07
- Dynamic programming Neural network presentation
Reinforcement-Learning
- 基于神经网络的强化学习是对强化学习算法的一种改进，本文讲述了将基于神经网络的强化学习算法用于移动机器人动态导航。-Reinforcement learning based on neural network is an improvement of reinforcement learning algorithm. This paper describes the reinforcement learning algorithm based on neural network for dynam
Sliding-Mode-Control
- 系统论述了滑模变结构控制的基本理论、基本方法和应用技术，并全部给出了MATLAB仿真分析。全书内容涵盖了滑模变结构控制发展综述、连续系统滑模控制、自适应鲁棒滑模控制、欠驱动系统滑模控制、反演及动态面滑模控制、基于滤波器及观测器的滑模控制、离散系统滑模控制、模糊滑模控制、神经滑模控制和机械手滑模控制等。-The basic theory, basic method and application technology of sliding mode variable structure contr
Study-on-a-novel-approach-to-active-power-filter.
- This paper proposes an effective control scheme for three-phase three-wire (3P3W) active power filter using neural-based harmonic identification scheme. To achieve excellent steady state and dynamic response, the feedback control plus feed-fo
c72e5dd2127eecc688cd6c5d0299e28b3ab1
- This paper focuses on the design of a novel control approach. Its contribution to the existing literature is that in the design of an interval type-2 fuzzy neural system, recursive fuzzy c-means clustering algorithm is used and the designed algorithm