swnet

gud code in matlab..make it use of it..it ju segments different images aand make it useful for classifying

matlab上进行FFT变换的程序，为初学者提供有用的帮助(matlab fft)

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. */

说明：  插值和拟合算法实验源代码，希望给大家带来帮助。(Interpolation and the fitting method experimental source code, hope to bring you help. )

heat equation matlab file

单输入单输出模型预测控制算法实现--一般实现，逆响应和线性规划(SISO model predictive control- a general approach, inverse response and linear programming)

PULSED STRIPMAP SAR SIMULATION AND RECONSTRUCTION, u domain parameters and arrays for SAR signal, u domain parameters and arrays for compressed SAR signal, Fast-time domain parameters and arrays Resolution Parameters of Targets, SIMULATION, This code performs "narrow-bandwidth" digital spotlighting.To improve results, convert the code to "wide-bandwidth" , 2D BEAM PATTERN MATCHED FILTERING SLOW-TIME, DOPPLER SUBSAMPLING RECONSTRUCTION

将一个512×512的lena.bmp图像进行DCT变换，对得到的DCT系数按8×8块进行分层（分三层），重组填充得到新的系数矩阵，结果发现原图像的能量集中在了新图像的左上角低频部分。(The lena.bmp an image of 512 × 512 is subjected to DCT transform, the DCT coefficients obtained by layering 8 × 8 block (of three), to obtain a new recombinant filling coefficient matrix, the energy of the original image is found concentrated in upper left corner of the low-frequency part of the new image.)

LS-SVM Leave-One-Out Cross-Validation Demo G. C. Cawley, "Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs", Proceedings of the International Joint Conference on Neural Networks (IJCNN-2006), pages 1661-1668, Vancouver, BC, Canada, July 16-21 2006. (pdf)theoval.cmp.uea.ac.uk/~gcc/matlab/

一种改进的萤火虫算法解决动态0-1背包问题。经过测试，算法就有良好的性能。(Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to uate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.)