【预测模型】基于模糊神经网络实现嘉陵江水质评价预测matlab源码.md

Matlab

针对传统PID整定控制效果差且单纯神经网络整定存在参数学习和调整困难等问题，提出了一种基于改进模糊神经网络的PID参数整定方法。在该方法中，PID控制器的控制参数采用基于Mamdani模型的模糊神经网络进行自适应整定，模糊神经网络参数采用混沌遗传算法离线粗调和BP算法在线细调的方式进行学习和调整，仿真结果表明该整定策略动态响应快、误差控制精度高且网络中各节点及参数物理意义明确。最后分别从模糊规则数的变化及适应度函数的选取两方面提出两种优化方案，仿真结果表明增加模糊规则数或采用不同的适应度函数都有利于进一步减小控制误差。

matlab 模糊神经网络 代码 fnn FnnSimu 功能 模糊神经网络仿真函数。 格式 retstr = FnnSimu(kd,sj,td)。 说明 调用训练好的模糊神经网络模型，对输入样本进行仿真，各参数说明如下： (1) kd 输入参数，学习阈值。 (2) sj 输入参数，学习进度。 (3) td 输入参数，仿真输入数据。

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

针对常规PID控制器和模糊PID控制器存在控制精度差、不能自适应、模糊规则难以确定等问题，本文提出一种基于RBF模糊神经网络的PID自整定控制算法，RBF模糊神经网络参数先采用遗传算法粗调，达到预定精度后，继续使用BP算法提高精度。通过在MATLAB中进行神经网络训练和PID仿真实验，表明了改进RBF模糊神经网络PID控制器具有收敛速度快、能够自适应、控制精度高等优点，具有一定的可行性。

模糊神经网络工具箱应用的一个实例，对初学者有一定用处

基于SIMULINK的汽车发动机怠速模糊神经网络控制-1.rar 这是师兄的一篇论文—基于SIMULINK的汽车发动机怠速模糊神经网络控制  我拿出来分享！希望对大家有所帮助！ 压缩文件夹里有几十个pdf 主要内容 QQ截图未命名.jpg

这是发表在自动化学报上的模糊神经网络函数逼近的论文，感觉挺不错

基于自适应模糊神经网络的非线性系统模型预测控制