genetic-algorithm

Power flow analysis in two bus system

A 4 bit twisted ring counter is a sequential circuit which produces the following sequence of output values: 0000, 1000, 1100, 1110, 1111, 0111, 0011, 0001 and then repeats. Design a circuit for a 4 bit twisted ring counter that uses four D flip flops. Draw a state transition diagram, a state table and a schematic for your circuit. Design an alternate implementation using just three flip flops and draw a state transition diagram, state table and a schematic for your circuit. If your designs are extended to implement an n bit twisted ring counter, how many flip flops are required using each of the two approaches. In what situations would you prefer the first method? In what situations would you prefer the second?

光伏阵列在不均匀光照情况下的多峰输出模型(the model of the PV in the partially shadow)

FCM聚类算法 已经通过调试，可以直接使用。(FCM clustering algorithm has been through debugging, can be used directly.)

小波分析理论与MATALB R2007 实现第十一章演示代码(The wavelet analysis theory and MATALB R2007 A Chapter XI demo code)

GOOGLE的图像检索程序，应用MATLAB编写，简单易懂，适用于初学者(GOOGLE image retrieval procedures, using MATLAB to prepare, easy to understand for beginners)

beamformer code using c lang

说明：  层次分析法求解,这是个MATLAB中可以直接运行，输入判断矩阵，即可求解(AHP method, this is a MATLAB can be run directly, enter the matrix, can be solved)

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

Time Series Forecast With Neural Network and Wavelet Techniques