Gui_scripter

双级矩阵变换器的MATLAB的仿真实现，完整的验证了双级矩阵变换器的基本原理。(Two-stage Matrix Converter Simulation of MATLAB-)

IEEE30节点配电网标准系统图，可运行,用于配电网建模，电能质量分析(IEEE30 node distribution network standard system diagram, you can run.)

Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga, B. Basturk Akay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617/2007, 318-329, 2007. D. Karaboga, C. Ozturk, Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification, Neural Network World, 19(3), 279-292, 2009. */

Performs basic arithmetic while returning the fractional answer.

用感知准则函数的方法求解以下数据的判决面，学习率为 ，画出每次迭代法向量的变化轨迹，并画出最终的判决曲线。 (The Perceptron criterion function method for solving the decision following data side, learning rate, draw vector for each iteration the trajectory, and draw the curve of the final decision.)

matlab中实用的静止无功补偿装置的仿真电路，主要用到了晶闸管控制电抗器和晶闸管投切电容器(in practical circuit simulation matlab static reactive power compensation device, mainly used in the thyristor controlled reactor and thyristor switched capacitor)

《Sparse and Redundant Representations：From Theory to Applications in Signal and Image Processing》Michael Elad的电子书，讲稀疏表示及其应用(《Sparse and Redundant Representations：From Theory to Applications in Signal and Image Processing》Michael Elad )

bpsk调制解调matlab代码，给出了在噪声信道下bpsk调制解调的matlab实现(bpsk modulation and demodulation matlab code is given in the noise channel bpsk modem implementation in matlab)

绘制经验函数图像的Matlab程序，用于数理分析(Experience of Matlab functions to draw the image program for mathematical analysis)

介绍线性预测编码(LPC)及其基本原理，由语音信号常用的全极点模型的传递函数推导出线性预测方程，(Introduction of linear predictive coding (LPC) and the basic principles of commonly used by the speech signal transfer function of all-pole model of a linear prediction equation is derived,)