jseasyui

说明：  英国伦敦大学一个学生写的有关UWB的文章，里面有较详细的说明并附有Matlab源码，适合初学者。(University of London a student to write an article on UWB, there are more detailed description with Matlab source, suitable for beginners.)

应用交替方向乘子法来求解L1正则化问题、BP问题、LASSO问题的一种算法，(Application alternating direction multiplier method to solve L1 regularization problem, BP issue LASSO problem an algorithm)

matlab中，时频峰值滤波处理地震记录时的窗长讨论代码(matlab window length, the time-frequency peak filtering processing of seismic records of discussions code)

The calculation of one segment kepstra Resco Consider a signal vm0.bin - raw data without the head? ky, fs = 16000 Hz, for on? reading to use the MATLAB loadbin.m

本文件内容是聚类算法，对判别个体进行分类，十大经典算法之一，除了程序以外，还有详细的讲解，便于理解。(The contents of the documents clustering algorithm, determine the individual classification, one of the top ten classical algorithm, in addition to the program, there are detailed explanations, easy to understand.)

2频移键控模拟模型的基本原理、波形,请您仔细浏览,给予评价(2 FSK simulation principle and waveform, please carefully browsing, giving the appraisal)

简介：IFP_FCM,改进型FCM(模糊C均值)聚类算法,由Frank Hoppner与Frank Klawonn于2003年提出,解决了FCM算法对初始值设定较为敏感、训练速度慢、在迭代时容易陷入局部极小的问题。并附带了Box和Jenkins煤气炉数据模型辨识实例。 文件清单： IFP_FCM算法原始论文： Improved fuzzy partitions for fuzzy regression models.pdf 程序清单： IFP_FCM.m - IFP_FCM算法主函数 initifp_fcm.m - initifp_fcm子函数 stepifp_fcm.m - stepifp_fcm子函数 distifp_fcm.m - distifp_fcm子函数 IFP_FCM_T_S_box_test.m - IFP_FCM测试实例：Box和Jenkins煤气炉数据T-S模糊模型辨识 数据文件： box_data.mat - Box和Jenkins煤气炉数据(IFP_FCM, modified FCM (Fuzzy C-Means) clustering algorithm, in 2003, proposed by Frank Hoppner and Frank Klawonn, FCM algorithm to solve the initial value setting is more sensitive, slow training, while iterating easy to fall into local minimum problem. And comes with Box and Jenkins gas furnace model identification data instance.)

现在谱分析的发展，详细介绍了谱分析的发展情况，建议学习谱分析的相关人士阅读(Now the development of spectral analysis, spectral analysis described in detail the development of the proposed study and spectral analysis of the relevant people to read)

本文提出一种新的智能故障诊断方法,基于统计分析,提出了一种改进的距离的评价技术和适应性类神经模糊推论系统(简称ANFIS)。该方法包括三个阶段。　(This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy)

files needed to read from text and to update output channels