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【AI-NN-PR】 pso-svm DL : 1: 利用PSO优化SVM，利用分组式训练方法提高算法速度-PSO to optimize the use of SVM, the use of packet-style training methods improve algorithm speed
Update : 2024-04-20 Size : 111616 Publisher : zhangqing
【AI-NN-PR】 PS0-SVR DL : 0: ：针对发酵过程中生物参数难以实时在线测量的问题，建立了用于生物参数状态预估的支持向量机软测量模型。考虑到该支持向量回归(SVR)模型的复杂性和冷化特征取决于其三个参数，c，能否取到最优值，采用粒子群优化(PSO)算法实现对参数，c，的同时寻优。在此基础上，以饲料用．甘露聚糖酶为对象，建立了基于PSO—SVR的发酵过程产物浓度状态预估模型。发酵罐控制结果表明：该模型具有很好的学习精度和泛化能力，可实现对．甘露聚糖酶产物浓度的实时在线预估。-In view of the hardship to get real—time and on—line biological parameters in fermentation process，a soft sensor model based on support vector machines is established for estimating the bio— logical parameters．The complexity and generalization performance of the support vector regression (SVR)model depend on a good setting of the three parameters ，c，．An algorithm called parti— cle swarm optimization(PSO)is applied to optimize the three parameters synchronously．Based on the proposed method，a PSO—SVR model is developed to estimate the products concentration of beta— mannanase for feedstuf．The control results of fermenter show that the state estimation model based on PSO·-SVR has good learn ing accuracy and generalization perform ance SO as to obtain the real·-time and on—line estimation for products concentration of beta—mannanase．
Update : 2024-04-20 Size : 231424 Publisher : 11
【matlab】 psoSVM DL : 0: 利用微粒群算法（pso）优化支持向量机（SVM）的参数-c,-g-Using pso optimized support vector machine parameters-c,-g
Update : 2024-04-20 Size : 1024 Publisher : 刘明丹
【matlab】 FeatureSelection_MachineLearning DL : 0: Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.-Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.
Update : 2024-04-20 Size : 6144 Publisher : Gang Fu
【matlab】 psopetsvr DL : 0: 一个用于预测石油污染物降解率的SVR程序，模型参数采用PSO算法优化选择。供大家参考！-a SVR model for prediction of oil degradation rate. Model s parameters was optimized by PSO algorithm.
Update : 2024-04-20 Size : 2048 Publisher : 雷柯
【Windows Develop】 2016.11.03DE_SVR（差分进化） DL : 1: 以优化SVR算法的参数c和g为例，对DE（差分进化）算法MATLAB源码进行了详细中文注解。(Differential Evolution algorithm (DE) is a heuristic random search algorithm based on group differences. This algorithm is proposed by R.S and k.p. rice for solving Chebyshev polynomials. DE algorithm is also an intelligent optimization algorithm, which is similar to the previous heuristic algorithm, such as ABC, PSO, etc., which is a heuristic optimization algorithm. The DE algorithm is an optimization algorithm that I used in solving the case study of box coverage.)
Update : 2024-04-20 Size : 5120 Publisher : mercy认真的雪
【matlab】 PSO-Based-SVR-master DL : 0: 该文件为粒子群算法优化支持向量机模型(This document is optimized by Particle Swarm Optimization (SVM) model)
Update : 2024-04-20 Size : 28672 Publisher : zhouyucai
【AI-NN-PR】 pso-svr代码 DL : 0: 基于粒子群优化的向量回归预测分析 matlab代码(Support vector regression code with pso)
Update : 2024-04-20 Size : 1024 Publisher : 1adams
【Documents】 PSO-optimized-SVR-master DL : 1: POS-SVR开源代码里面有举例可以跟着小例子试着做且有部分注释(POS-SVR Open Source contains examples that can be tried with small examples and some comments)
Update : 2024-04-20 Size : 16384 Publisher : leibovo
【Other】 svr_pso - Libsvm DL : 0: SVR optimizaton using PSO with LibSVM library
Update : 2024-04-20 Size : 106496 Publisher : Malek01
【matlab】 PSO SVM粒子算法优化的支持向量机 DL : 0: 使用PSO优化SVR支持向量机模型的代码，有详细的输出及输出，以及代码说明(Use PSO to optimize SVR support vector machine model code, with detailed output and output, as well as code description)
Update : 2024-04-20 Size : 1024 Publisher : YYYONG2018