intelligent-control

一个基于matlab与vc混合编程小例子,对初学者非常有利(a Matlab-based program mixed with vc small examples, very beneficial for beginners)

Steganography project source code in matlab..

matlab下的adaptfilt，用于自适应滤波器，滤波信号堪比输入无噪音信号(matlab under adaptfilt, for the adaptive filter, filtering the signal comparable to the input signal without noise)

poisson 求解 Benchmarks several different ways of generating Compound Poisson random variables by Monte Carlo simulation.(compound poisson Benchmarks several different ways of generating Compound Poisson random variables by Monte Carlo simulation.)

三相整流后逆变，电流滞环控制永磁电机，效果还不错(After the three-phase rectifier inverter, the current hysteresis control of permanent magnet motor, effect is good)

说明：  通过 matlab 编程实现捷联惯导系统初始对准、惯导解算的全过程。 1）初始自对准：利用前10分钟实验车静止的数据进行惯导系统的自对准。采用前2分钟数据进行解析粗对准，后8分钟数据用五状态Kalman滤波器实现精对准；为了验证精对准性能，可在粗对准结果上把航向角人为加一定幅度（如10度）的误差，观察精对准的收敛性能。 2）纯惯导解算：对准结束后转入纯惯性导航解算，要求进行双子样的圆锥和划摇补偿，即每20ms完成一次导航解算，输出完整的导航结果包括三个姿态、两个水平速度和两个水平位置（天向通道发散不必输出），并给出姿态、速度、位置随时间的变化曲线以及位置误差曲线。(The whole process of initial alignment and inertial navigation solution of strapdown inertial navigation system is realized by matlab programming.)

system view 实例模型包含数字调制QPSK,QAM.RICE衰落信道的仿真 数字频率合成器 线性移位寄存器 等等(system view instance of the model consists of digital modulation QPSK, QAM.RICE fading channel simulation of digital frequency synthesizer linear shift register, etc.)

用于激光谐振腔的计算，采用早期的foxli算法。

小波稀疏分解和重构算法，适合通信信号和图像重构(Sparse wavelet decomposition and reconstruction algorithm for communications signal and image reconstruction)

本文采用小波神经网络进行交通流量预测，短时交通流量存在随机性和非线性因素，影响预测的准确性。传统预测模型难以反映交通流量变化特点，同时传统神经网络易陷入局部极小值，泛化能力差，交通流量预测精度低。为了提高短时交通流量预测精度，提出一种小波神经网络的短时交通流量预测模型。小波神经网络可以对短时交通流量随机性、不确定性进行局部分析，并进行非线性预测，验证了模型的有效性，进行了对比试验。验证结果表明，小波神经网络提高了短时交通流量预精度，预测结果更具应用价值。(In this paper, wavelet neural network is used to forecast traffic flow. There are random and nonlinear factors in short-term traffic flow, affecting the accuracy of prediction. The traditional prediction model is difficult to reflect the characteristics of traffic flow changes. At the same time, the traditional neural network is easy to fall into the local minimum, the generalization ability is poor, and the prediction accuracy of traffic flow is low. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow prediction model based on wavelet neural network is proposed. The wavelet neural network can analyze the randomness and uncertainty of short-term traffic flow and perform nonlinear prediction. The validity of the model is verified and a comparative experiment is conducted. The verification results show that the wavelet neural network improves the short-term traffic flow pre-precision, and the prediction result has more application value.)