Skip to content
View gabrielegilardi's full-sized avatar

Block or report gabrielegilardi

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
gabrielegilardi/README.md

Repositories (from scratch) about data structures, fuzzy logic, machine learning, metaheuristic optimization, and robotics.

C++

ANFIS-metaheuristic - Multivariate regression and classification using an adaptive neuro-fuzzy inference system (Takagi-Sugeno) and metaheuristic optimization.

BayesianInference - Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.

SimulatedAnnealing - Implementation of metaheuristic optimization using population-based simulated annealing.

Python

ANFIS - Multivariate regression and classification using an adaptive neuro-fuzzy inference system (Takagi-Sugeno) and particle swarm optimization.

BayesianInference - Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.

BinaryTree - Binary tree data structure using a binary node data structure.

Clustering - Implementation of K-means and fuzzy C-means clustering methods using a naive algorithm and particle swarm optimization.

DataStructures - Basic data structures (stack, queue, priority queue, binary heap.)

DecisionTree - Regression using decision tree, random tree, bootstrap aggregating (bagging), and boosting.

FeedForwardNN - Multivariate regression and classification using a feed-forward neural network and gradient descent optimization.

GridSearch - Two-dimensional grid search using depth first search, breath first search, A* algorithm, and Dijkstra’s algorithm.

HashTable - Hash table and dictionary class implementation using lists and double-linked lists.

LinkedLists - Single and double linked list data structures.

PathPlanning - Implementation of particle swarm optimization (PSO) for path planning when the environment is known.

PSO - Metaheuristic minimization using particle swarm optimization.

Q-Learning - Reinforcement learning using Q-learning, double Q-learning, and Dyna-Q.

RegressionGDO - Multivariate linear and logistic regression using gradient descent optimization.

SignalFilters - Signal filtering and generation of synthetic time-series.

SimulatedAnnealing - Implementation of metaheuristic optimization using population-based simulated annealing.

Sorting - Implementation of sorting and searching functions for lists and arrays.

SpaceDyn - A toolbox for space, mobile, and humanoid robotic systems (work in progress).

Matlab

ClassificationNN - Multivariate classification using a feed-forward neural network and backpropagation.

FingerControl - Biomimetic Control of an Artificial Finger for Rehabilitation Robotics Using Shape Memory Alloy Actuators.

Pinned Loading

  1. PathPlanning PathPlanning Public

    Implementation of particle swarm optimization (PSO) for path planning when the environment is known.

    Python 80 22

  2. ANFIS ANFIS Public

    Multivariate Regression and Classification Using an Adaptive Neuro-Fuzzy Inference System (Takagi-Sugeno) and Particle Swarm Optimization.

    Python 44 12

  3. SignalFilters SignalFilters Public

    Signal Filtering and Generation of Synthetic Time-Series.

    Python 11 11

  4. PSO PSO Public

    Metaheuristic Minimization Using Particle Swarm Optimization.

    Python 11 2

  5. SpaceDyn SpaceDyn Public

    A Toolbox for Space, Mobile, and Humanoid Robotic Systems (Pyhton version of the Matlab code from the Space Robotics Lab. at Tohoku University, Sendai, Japan) - Work in progress.

    Python 4 2

  6. Clustering Clustering Public

    Implementation of K-means and fuzzy C-means clustering using the naive algorithm and particle swarm optimization.

    Python 3 2