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一种基于模糊径向基函数神经网络的自学习控制器-based on fuzzy RBF neural network learning controller.
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有脉冲干扰 下的柔性机械臂的模糊控制方法。采用神经网络训练出来的T—S模糊控制器,控制效果不错。只有仿真模块,没有文字说明。-pulse interference with the interference of fuzzy manipulator control methods. Using neural network training, the T-S fuzzy controller, control effectiveness. Only Simulation Module, ther
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为了便于进行形象化的理解,本章进行了控制结果的可视化编程。利用Matlab与Borland C++ Builder的程序开发接口,通过建立独立的可执行程序,演示了控制过程中对熨平板进行调节的过程。最终认为,PID控制和人工神经网络控制对控制速度和精度都有了很多的改善;对摊铺机自动调平装置而言,其控制器的设计占有重要的地位。 -To facilitate figurative understanding of this chapter for the control of the results
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智能控制仿真4 基于神经网络的模糊控制器设计 简单易懂 适合学习-four intelligent control simulation based on neural network fuzzy controller design is simple to understand for learning
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BP神经网络结构PID控制器设计,严格按照增加动量项写的MATLAB程序。,Structure of BP neural network PID controller design, in strict accordance with the increasing momentum of procedures written in MATLAB.
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S函数的BP神经网络PID控制器Simulink仿真,S function of BP neural network PID controller Simulink simulation
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BP神经网络算法,采用遗传算法训练具有7个隶属函数的FNN控制器权值的程序等-BP neural network algorithm, using genetic algorithm training with seven membership function of the FNN controller weighting procedures
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将基于聚类分析的神经网络-模糊控制器应用于火电厂锅炉控制系统的访问研究-Cluster analysis will be based on neural network- fuzzy controller used in thermal power plant boiler control system access to research
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摘要:给出了解决机器人控制问题一种神经网络方法。使用一个分级神经网络(NN)结构学习刚体机器人动力学特点。对于一般类别的机械手,使用前训练一系列的三层前馈网络模块,然后把这些基函数实时地用于第四层。使用线性控制原理,辅以非线性补偿控制机械手,使学得的机械手动力学知识创建一个在整个工程中高速控制机械手的控制器。模拟结果表明控制器的性能得到了大大提高。-Abstract: This paper presents a solution to the issue of robot control neu
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一种基于模糊神经网络的控制策略,融合了模糊控制和神经网络的优点,根据变量的动态调整实施控制器的动态参数调整-Based on fuzzy neural network control strategy, the convergence of fuzzy control and neural network advantages, in accordance with the dynamic adjustment of the implementation of variable controll
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关于Mass-Damper-Spring System 的鲁棒控制源代码-About Mass-Damper-Spring System Robust Control of source code
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小论文里的程序,用RBF神经网络优化PID控制器的,效果非常好!-Small in the thesis process, optimize the use of RBF neural network PID controller, the effect is very good!
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提出了一种新颖的控制方案,即采用神经网络预测控制器来控制机器人的力/位置.这种控
制器能任意逼近机器人这种不确定对象,不用知道系统的精确结构,同时由于预测控制的加入,使系
统在线计算方便,控制质量提高了-A novel control scheme, namely, the use of neural network predictive controller to control the robot s force/position. This control
Syste
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RBF神经网络用s函数编程的,主要功能是用作控制器-RBF neural network with programming function s main function is used as a controller
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用MATLAB实现的神经网络控制器设计程序以及性能测试仿真程序(MATLAB neural network controller design procedure and the performance test simulation program)
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使用labview编写的神经网络控制器。(A neural network controller written using labview.)
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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of t
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根据所学过的BP网络设计及改进方案设计实现模糊控制规则为T = int((e+ec)/2)的模糊神经网络控制器,其中输入变量e和ec的变化范围分别是:e = int[-2, 2],ec = int[-2, 2]。网络设计的目标误差为 0.001。(According to the BP network design and improvement plan that we have learned, we design a fuzzy neural network controller with
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用于对比分析三种励磁方式下控制器性能比较,有常规控制器,模糊控制器,神经网络控制器。(Used for comparative analysis of three kinds of excitation mode controller performance comparison,a conventional controller, fuzzy controller, neural network controller.)
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实现控制规则为T=int((e+e1)/2)的模糊神经网络控制器(A fuzzy neural network controller with T=int ((e+e1) /2) control rule is implemented.)
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