jiachuang

Simulink files: · HeaterTest.mdl Heater test model in Simulink(Requires HdataM) · TSKHeater.mdl Heater control using TSK alg(Requires HdataM,HdataF,HdataTd) · MamdaniTest.mdl Simple Mamdani test model(Requires Mdata) · Mamdani9Test.mdl 9 rule Mamdani test model(Requires Mdata) · Mamdani25Test.mdl" 25 rule Mamdani test model(Requires Mdata25) · EngineTest.mdl Vehicle engine test model in Simulink(Requires VdataM,VdataC) · MamdaniVehicle.mdl Vehicle control via simple Mamdani FLC(Requires VdataM,VdataF) · Mamdani9Vehicle.mdl Vehicle control via 9 rule Mamdani FLC(Requires VdataM,VdataF) · Mamdani25Vehicle.mdl Vehicle control via 25 rule Mamdani FLC(Requires VdataM,VdataF25) · OptimalVehicle.mdl Vehicle control via simple LQ alg(Requires VdataM,VdataC)

随机减量技术（Random Decrement Technique-RDT），其主要是利用样本平均的方法，去掉响应中的随机成分，而获得初始激励下的自由响应，(Random decrement technique (Random Decrement Technique-RDT), whose main method is to use the sample mean, remove the random component of the response, and the response to the initial excitation freedom of)

Split PML 2D TM FDTD

衰落信道数值的生成，用于模式识别，具有一定的参考价值(Fading channel to generate value for pattern recognition, with some reference value)

MATLAB，由浅入深 源代码 打包压缩 ( code example)

Reinforcement Learning 中以 Q learning 學習的倒單擺實驗。以x,x_dot,theta,theta_dot作為狀態參數 state，利用model模擬之結果做Q值更新，產生Q table，對學習平衡控制。結果以csv檔作為輸出，可以由使用者作圖觀察變化。(Reinforcement Learning , utilize Q learning for An inverted pendulum system control。x,x_dot,theta,theta_dot as state variable，use model result renew Q table，control learning of balance pole 。output csv file contain trail(times),balance time(sec).it s better observe the change if user plot it. )

affine transform image scrambling

mrvm是RVM的改进，可直接用于多分类。不用什么一对一之类的。效率更高更好使。(Mrvm is an improvement of RVM and can be directly applied to multi classification. No need for one to one. Efficiency is better and better.)

MATLAB用于非线性方程组的求解问题，用的方法是牛顿迭代法(the solution of nonlinear functions)

matlab程序：高斯核回带用法的支持向量机，对样本经行训练得到预测模型(matlab program: the Gaussian kernel back with the use of support vector machine prediction model of the samples by the line training)