基于MATLAB写的可对遥感影像进行BP神经网络分类的m文件，里面有测试图像数据，其中感兴趣区域数据是由ENVI选取的感兴趣区域保存而来。

集合了卷积神经网络从神经网络分类Alnex,GoogleNet v1-v4,VGG,Resnet,Network in Network论文，图像检测R-CNN,FAST-RCNN,Faster-rcnn，Mask-rcnn，SSPN-net，SSD,YOLO，YOLO_v2,YOLO_v3

matlab的概率神经网络分类器，可以作为参考，能够看明白最好

简单的基于MATLAB的手写字母识别（神经网络分类器）程序，想具体了解可以查看我的博客。

基于 LVQ神经网络的分类——乳腺肿瘤诊断

完整代码，可直接运行

本资源附有配套的7篇博客辅助讲解。 教程博客地址为：https://blog.csdn.net/qq_43592352/article/details/122960985 代码架构强，非常易于理解。 代码拓展性强，方便移植使用自己的数据集、模型。 代码主要采用pytorch实现。

概率神经网络的分类预测-基于PNN变压器故障诊断 matlab程序

JAVA_BP神经网络分类器.7z

It is known that there is no sufficient Matlab program about neuro-fuzzy classifiers. Generally, ANFIS is used as classifier. ANFIS is a function approximator program. But, the usage of ANFIS for classifications is unfavorable. For example, there are three classes, and labeled as 1, 2 and 3. The ANFIS outputs are not integer. For that reason the ANFIS outputs are rounded, and determined the class labels. But, sometimes, ANFIS can give 0 or 4 class labels. These situations are not accepted. As a result ANFIS is not suitable for classification problems. In this study, I prepared different adaptive neuro-fuzzy classifiers. In the all programs, which are given below, I used the k-means algorithm to initialize the fuzzy rules. For that reason, the user should give the number of cluster for each class. Also, Gaussian membership function is only used for fuzzy set descriptions, because of its simple derivative expressions The first of them is scg_nfclass.m. This classifier based on Jang’s neuro-fuzzy classifier [1]. The differences are about the rule weights and parameter optimization. The rule weights are adapted by the number of rule samples. The scaled conjugate gradient (SCG) algorithm is used to determine the optimum values of nonlinear parameters. The SCG is faster than the steepest descent and some second order derivative based methods. Also, it is suitable for large scale problems [2]. The second program is scg_nfclass_speedup.m. This classifier is similar the scg_nfclass. The difference is about parameter optimization. Although it is based on SCG algorithm, it is faster than the traditional SCG. Because, it used least squares estimation method for gradient estimation without using all training samples. The speeding up is seemed for medium and large scale problems [2]. The third program is scg_power_nfclass.m. Linguistic hedges are applied to the fuzzy sets of rules, and are adapted by SCG algorithm. By this way, some distinctive features are emphasized by power values, and some irrelevant features are damped with power values. The power effects in any feature are generally different for different classes. The using of linguistic hedges increase the recognition rates [3]. The last program is scg_power_nfclass_feature.m. In this program, the powers of fuzzy sets are used for feature selection [4]. If linguistic hedge values of classes in any feature are bigger than 0.5 and close to 1, this feature is relevant, otherwise it is irrelevant. The program creates a feature selection and a rejection criterion by using power values of features. References: [1] Sun CT, Jang JSR (1993). A neuro-fuzzy classifier and its applications. Proc. of IEEE Int. Conf. on Fuzzy Systems, San Francisco 1:94–98.Int. Conf. on Fuzzy Systems, San Francisco 1:94–98 [2] B. Cetişli, A. Barkana (2010). Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Computing 14(4):365–378. [3] B. Cetişli (2010). Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1. Expert Systems with Applications, 37(8), pp. 6093-6101. [4] B. Cetişli (2010). The effect of linguistic hedges on feature selection: Part 2. Expert Systems with Applications, 37(8), pp 6102-6108. e-mail:bcetisli@mmf.sdu.edu.tr bcetisli@gmail.com