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anfis-elm-pso

The combination of ANFIS, ELM, and PSO

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

autoxgb

XGBoost + Optuna

dask-xgboost

elm

ELM为极限学习机，主要用于回归预测和分类

fft_test

FFT test project of Vivado

ga-ann

Use genetic algorithm to optimize the backpropagation neural network.

ga-ann-2

Hybridisation of Genetic Algorithm and Artificial Neural Network using MATLAB

ga-bp

基于遗传算法的BP网络设计，应用背景为交通流量的预测

ga-bp-2

大学论文找的遗传算法程序

ga_elm

Extreme Learning Machine (Neural Network) with Regularization Parameters optimized by a Genetic Algorithm. For my "Natural Computing" Class.

ga_pso_bp

intelligentoptimizationalgorithms

This repository displays the demos of some Intelligent Optimization Algorithms, including SA (Simulated Annealing), GA (Genetic algorithm), PSO (Particle Swarm Optimizer) and so on. And some other algorithms will be appended in the future.

matlab-machine

peiran-peng-project

GWO-ELM and GA-ELM in MATLAB for CIGS solar cells prediction

project_rossmann_sales_prediction

预测rossmann1115家商店未来的销售额

pso-ann

Hybridisation of Particle Swarm Optimisation and Artificial Neural Network using MATLAB

pso-dbn

By using 3-layer DBN to extract features embed in original image data, then fed the features into a extreme learning machine(ELM) for classification. Due to the difficulty in hiiden-nodes selection， then PSO was applied to select it automatically ,and PSO aimed to minimize the fitness function that is the accuracy of DBN-ELM crossvalidation classif

pso-ielm

Incremental ELM with PSO algorithm

reconstruct-tropomi-sif

This contains the code for generating the RTSIF dataset and training the XGBoost model

sleep-stage-identification

Project for identify human sleep stage using the combination of ELM and PSO compared to the combination of SVM and PSO

time-series-arima-xgboost-rnn

Time series forecasting for individual household power prediction: ARIMA, xgboost, RNN

wrapper-feature-selection-toolbox

This toolbox offers more than 40 wrapper feature selection methods include PSO, GA, DE, ACO, GSA, and etc. They are simple and easy to implement.

xgb_vegetation_mapping

This study, using extensive features and abundant vegetation survey data, created a workflow by adopting a promising classifier, eXtreme Gradient Boosting (XGBoost)

xgboost_predict