Learning-based robust tube based MPC of nonlinear systems via difference of convex radial basis functions

Deep Learning using Neural Network Toolbox + Finance Portfolio Selection with MorningStar

A collection of Matlab routines for illustrating methods for identifying Radial Basis Function (Neural) Network models for NARX-type nonlinear dynamical systems from data, incorporating prior information about the system's fixed points.

MATLAB implementations of different learning methods for Radial Basis Functions (RBF)

通过RBF基函数进行网格变形，考察了naca0012翼型的S型启动问题

Given a dataset containing car attributes, use MLP and RBF networks to predict the Miles per Gallon consumption

This repository contains a MATLAB-based Machine Learning Software (MLS) offers advanced biomedical signal processing with an intuitive GUI for analyzing EEG, ECG, and EMG. Features include noise filtering, feature extraction, dimensionality reduction, and customizable machine learning algorithms for tailored classification and analysis.

Implementation of MLP and RBF

Approximation of mixing different gaussian distribution with Self-organizing Map(SoM) and Radial Basis Function(RBF)

University Project for "Intelligent Systems" course (MSc Computer Engineering @ University of Pisa). Design and implementation of several artificial intelligences (MLP, RBFN, FIS, CNN, RNN) on a dataset composed on biophysical signals

A collection of Matlab routines for constructing Radial Basis Function (Neural) Network models of NARX-type nonlinear dynamical systems from data. It also includes the possibility of incorporating prior information about the underlying system's steady states in the structure selection of the RBF models.

Two pattern classification problem using Radial Basis Functions (RBF) Neural Networks, with center vectors selected via self-organizing map (SOM) neural networks.

Exercises and assignments made during a Computational Intelligence class at Federal University Of Ceará.

Repositório destinado aos projetos realizados na disciplina de Reconhecimento de Padrões (RP) [2023.1] do PPGEEC.