▍1. APSO
自适应粒子群算法matlab代码,好用。(The matlab code of adptive PSO algorithm.)
更多选项
▍2. bpa
基于bpa神经网络的曲面拟合matlab算例，注释详尽(a helpful bpa demo based on matlab with note in detail)
▍3. LSTM预测
说明： 使用LSTM对销售数据（吉恩销售数据集）进行预测(Use LSTM to forecast sales data)
▍4. GRNN-
基于广义回归神经网络的数据预测，使用交叉验证的GRNN神经网络预测程序，包含BP和GRNN效果比较程序。两网络用相同的数据进行训练。(Based on generalized regression neural network data prediction, using cross-validation GRNN neural network prediction program, including BP and GRNN effect comparison procedures. Two networks use the same data for training.)
▍5. GRNN-
基于广义回归神经网络的数据预测，使用交叉验证的GRNN神经网络预测程序，包含BP和GRNN效果比较程序。两网络用相同的数据进行训练。(Based on generalized regression neural network data prediction, using cross-validation GRNN neural network prediction program, including BP and GRNN effect comparison procedures. Two networks use the same data for training.)
▍6. EPON
完成了EPON上行数据的传输，实现了动态带宽分配算法(this file complete a basic upstream data transmission of EPON MAC protocol. )
▍7. EPON
完成了EPON上行数据的传输，实现了动态带宽分配算法(this file complete a basic upstream data transmission of EPON MAC protocol. )
▍8. VF
说明： 无线传感器网络布局优化的粒子群导向虚拟力算法仿真程序，有程序分析和解释。 (Wireless sensor network layout simulation program oriented virtual force algorithm of particle swarm optimization, program analysis and interpretation. )
▍9. 遗传算法在tsp中的应用
遗传算法在tsp中的应用(genetic algorithm in the application tsp)
▍10. inverted-pendulum-control
利用强化学习的自适应动态规划中的值迭代和策略迭代方法，神经网络控制方法，LQR状态调节器最优控制方法，实现了三维倒立摆在飞行器上的稳定控制。鲁棒性很强，进行了高斯白噪声的扰动实验。(Reinforcement learning adaptive dynamic programming in value iteration and policy iteration method, neural network control method, LQR state regulator optimal control method to achieve a three-dimensional inverted pendulum on the vehicle stability control. Very robust, conducted experiments Gaussian white noise disturbance.)
▍11. 拉丁超立方抽样工具箱
说明： 基于MATLAB软件的拉丁超立方抽样工具箱，亲测有效！(Latin hypercube sampling toolbox based on MATLAB software, pro test effective!)
▍12. CEC_2011_Matlab
2011年进化计算年会数值优化问题Matlab代码(Testing Evolutionary Algorithms on Real-world Numerical Optimization Problems，A competition for CEC2011)
▍13. DBN代码
MATLAB实例与人工智能神经网络深度学习深度信念网络(MATLAB instance and artificial intelligence neural network deep learning deep belief network)
▍14. ACSForCVRP(parallelPcandidateLists)
蚁群系统算法求解车辆路径问题，其中的蚁群系统使用了基于三角划分的候选列表。(Ant System algorithm for vehicle routing problem, which the ant colony system uses a list of candidates based on triangulation.)
▍15. ACSForTSP(sequence)
蚁群系统算法求解TSP,蚁群系统算法在构建路径时采用顺序构建的方式。(Ant System Algorithm for TSP, ant colony system algorithm constructed using sequential manner during the build path.)
▍16. fnn
模糊神经网络的一个例子，很详尽，可以根据自己的实际项目修改(Fuzzy neural network An example of a very detailed, you can according to their actual project modifications)
▍17. RVFL
说明： RVFL是一种简单的神经网络，初始参数随机确定，仅需训练隐层至输出的权重。(RVFL is a simple neural network, the initial parameters are randomly determined, and only the hidden layer is trained to the weight of the output.)
▍18. SVM_GUI_3.1[mcode]{by-faruto}
支持向量机SVM(Support Vector Machine）作为一种可训练的机器学习方法，依靠小样本学习后的模型参数进行导航星提取，可以得到分布均匀且恒星数量大为减少的导航星表。 基本情况 Vapnik等人在多年研究统计学习理论基础上对线性分类器提出了另一种设计最佳准则。其原理也从线性可分说起，然后扩展到线性不可分的情况。甚至扩展到使用非线性函数中去，这种分类器被称为支持向量机（Support Vector Machine，简称SVM）。支持向量机的提出有很深的理论背景。 支持向量机方法是在近年来提出的一种新方法。(Support Vector Machine SVM (Support Vector Machine) as a trainable machine learning methods, relying on small samples after learning Navstar extract model parameters can be distributed uniformly and greatly reduced the number of stars the guide star. Basic Vapnik et al in years of study on the basis of statistical learning theory, linear classifier proposed an alternative design best practices. The principle is also linearly separable from talking, and then extended to the case of non-linear. Even extended to use a nonlinear function to go, this classification is known as support vector machines (Support Vector Machine, referred SVM). Support vector machine made a deep theoretical background. Support Vector Machine method is a new method proposed in recent years.)
▍19. SVM_GUI_3.1[mcode]{by-faruto}
支持向量机SVM(Support Vector Machine）作为一种可训练的机器学习方法，依靠小样本学习后的模型参数进行导航星提取，可以得到分布均匀且恒星数量大为减少的导航星表。 基本情况 Vapnik等人在多年研究统计学习理论基础上对线性分类器提出了另一种设计最佳准则。其原理也从线性可分说起，然后扩展到线性不可分的情况。甚至扩展到使用非线性函数中去，这种分类器被称为支持向量机（Support Vector Machine，简称SVM）。支持向量机的提出有很深的理论背景。 支持向量机方法是在近年来提出的一种新方法。(Support Vector Machine SVM (Support Vector Machine) as a trainable machine learning methods, relying on small samples after learning Navstar extract model parameters can be distributed uniformly and greatly reduced the number of stars the guide star. Basic Vapnik et al in years of study on the basis of statistical learning theory, linear classifier proposed an alternative design best practices. The principle is also linearly separable from talking, and then extended to the case of non-linear. Even extended to use a nonlinear function to go, this classification is known as support vector machines (Support Vector Machine, referred SVM). Support vector machine made a deep theoretical background. Support Vector Machine method is a new method proposed in recent years.)
▍20. 卷积网络matlab实现
用卷积神经网络（CNN）进行人脸识别，matlab编程，可用。(Convolutional neural network (CNN) is used for face recognition, and MATLAB programming is available.)
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