Description: 人工智能中模糊逻辑算法
FuzzyLib 2.0 is a comprehensive C++ Fuzzy Logic library for constructing fuzzy logic systems with multi-controller support.
It supports all commonly used shape functions and hedges, with full support for the various types of Aggregation, Correlation, Alphacut, Composition, Defuzzification methods.
The latest version of the C++ Fuzzy Logic Class Library contains all the C++ source code and comes complete with a usage example for building a multi-controllers fuzzy logic model.-artificial intelligence, fuzzy logic algorithm FuzzyLib 2.0 is a comprehensi 've Fuzzy Logic C library for constructing fuzzy logic systems with multi-controller support. It supports all commonly used functions a shape nd hedges. with full support for the various types of Aggre the accounts, Correlation, Alphacut, Composition, Defuzzification methods. The latest version o f the C Fuzzy Logic Class Library contains all th e C source code and comes complete with a usage ex ample for building a multi-fuzzy controllers l ogic model. Platform: |
Size: 314368 |
Author:周荷 |
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Description: 为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani 型模糊推理规则控制器控制倒立摆系统稳定的基础上,
设计了一种更有效率的基于Sugeno 型模糊推理规则的模糊神经网络控制器。该控制器使用BP 神经网络和最小二乘法的混
合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani 型控制器的仿真对比,
表明该Sugeno 型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。-In order to improve the three-level control of inverted pendulum system response speed and stability, in the design of Mamdani-type fuzzy inference rules of the system controller to control the stability of inverted pendulum on the basis of a more efficient design based on Sugeno-type fuzzy inference rules of fuzzy neural network controller. The controller is the use of BP neural network and hybrid least squares training algorithm parameters can be accurately summed up the amount of input and output fuzzy membership function and fuzzy logic rules. Mamdani-type controller with a simulation comparison shows that the Sugeno-type fuzzy neural network controller for the three-tier control of inverted pendulum system with good stability and fast, as well as a higher control precision. Platform: |
Size: 551936 |
Author:月到风来AA |
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Description: Efficiency optimization of IPMSM with fuzzy logic controller and loss minimization algorithm. Good for simulation. Platform: |
Size: 18432 |
Author:william |
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Description: These set of programs are written to design a robust power system stabilizer for minimizing the effects of low frequency oscillations in electric power systems. A complete nonlinear model of the power system represented by a single machine connected to an infinite bus is developed in the Simulink environment. A fuzzy Power System Stabilizer is designed using the fuzzy logic toolbox of matlab and its parameters are tuned by a NEural Network controller. Platform: |
Size: 15360 |
Author:al-amin |
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Description: This paper presents fuzzy logic control of Doubly Fed Induction Generator (DFIG) wind turbine in a sample power system. DFIG consists of a common induction generator with slip ring and a partial scale power electronic converter. Fuzzy logic controller is applied to rotor side converter for active power control
and voltage regulation of wind turbine. Platform: |
Size: 379904 |
Author:mouloud bouaraki |
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Description: Inthispapz, design, simulation and experimental verification
of a self-leaning tizzy logic controller (SLFLC) snitahle for control of
nonlinear SCNO sys ma are described. The SLFLC contains a leaning
algorithm that udlizes a second-order reference made1 and a sensitivity
mcdel related to the fuzzy controller parameters. The effectiveness of the
proposed controller has been tested in tbe position control loops of two
chopper-fed dc servo systems, frst by simulation in the presence of a
backlash nonlinearity, then by experiment in the presence of a gravity.
dependent shaft load and fairly high static friction. The simulation and
expimental results have proved that the SLFLC provides desired closedloop
behavior and eliminates a steady-state position mar.
Keywords: Fuzzy Systems, Fuuy Control, Sensitivity Model,- Inthispapz, design, simulation and experimental verification
of a self-leaning tizzy logic controller (SLFLC) snitahle for control of
nonlinear SCNO sys ma are described. The SLFLC contains a leaning
algorithm that udlizes a second-order reference made1 and a sensitivity
mcdel related to the fuzzy controller parameters. The effectiveness of the
proposed controller has been tested in tbe position control loops of two
chopper-fed dc servo systems, frst by simulation in the presence of a
backlash nonlinearity, then by experiment in the presence of a gravity.
dependent shaft load and fairly high static friction. The simulation and
expimental results have proved that the SLFLC provides desired closedloop
behavior and eliminates a steady-state position mar.
Keywords: Fuzzy Systems, Fuuy Control, Sensitivity Model, Platform: |
Size: 378880 |
Author:Nada Mustafa |
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Description: Fuzzy Logic Controller Based Three-phase Shunt Active Filter for Line Harmonics Reduction using matlab simulation Platform: |
Size: 1217536 |
Author:akhi |
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Description: this static var compensator based on fuzzy logic controller-this is static var compensator based on fuzzy logic controller Platform: |
Size: 398336 |
Author:krishna |
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Description: 针对光伏发电系统的控制问题，提出一种模糊控制算法，提高效率。-Photovoltaic power generation system for the control problem, presents a fuzzy control algorithm to improve efficiency. Platform: |
Size: 614400 |
Author:Rick |
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Description: This package is only for optimization of the scaling factors.
It is assumed that the 5 triangular membership functions equally distributed with 25 rules.
This may not be the best way to construct the Fuzzy Logic Controller (FLC),
because the FLC is reconstructed at every simulation run and hence it may be much slower than a fixed FLC structure.
However, here we are able to optimize not only the input-output scaling factors, but also the the input/output membership function distribution and the rule-base.
Three sets of files will be uploaded eventually:
1- Only optimizes the scaling factors (T)
2- Only optimizes the distribution of the membership functions (S), assuming that “T”s are already optimized.
3- Only optimizes the rule-base (R), assuming that “T”s and “S”s are already optimized Platform: |
Size: 15360 |
Author:Murtadha |
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Description: AUV控制仿真，滑模控制，模糊控制，对于水下无人平台仿真有挺大的参考意义(AUV control simulation,
Sliding mode control, It has great reference significance for the simulation of underwater unmanned platform) Platform: |
Size: 828416 |
Author:小灰狼_58 |
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