GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN

使用粒子群算法优化的RBF神经网络进行预测。RBF neural network optimized by particle swarm optimization is used for prediction.

RBF(Radial Basis Function) Neural Network Implementation in Python

RBF神经网络自适应控制的相关代码，具体可参考 Liu J K. Radial Basis Function Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation.

Stock Market Prediction Using Neural Network Models (Backpropagation, RNN, RBF) Keras with Tensorflow backend

Python code of RBF neural network classification model

an implementation of a Radial Basis Function Neural Network (RBFNN) for classification problem.

RBF Neural Network Adaptive Control

使用RBF、BP神经网络进行预测。RBF/BP neural network is used for prediction.

the meteorological data and power generation data of one PV power station used in Ultra-short-term Forecasting of Photovoltaic Power via RBF Neural Network

Radial Basis Function Neural Network designed to predict house prices in the Boston area

paper code of "Adaptive feedforward RBF neural network control with the deterministic persistence of excitation"

RBF neural network was constructed using tensorflow framework

RBF Neural Network with Self Organazing Map to solve classification problem.

Training an RBF(Radial Basis Function) Neural Network for function approximation. (2019)

implementing a RBF neural network with lion pride and KNN clustering on the cancer dataset

This projects allows users to predict stock prices through the use of scikit-learn to train a support vector regression on a Google Finance dataset (apple in this case). The code produces a graph showing the 3 model used: RBF, Linear, and Polynomial (RBF turned out to be the best one). The Machine Learning model can be adjusted to Keras, as well, to adapt it to Neural Networks. A further upgrade might be prediction of stock prices by using sentiment analysis and price history.

Using Neural Networks in 3 datasets taken from the UCI database. Algorithms: Naive Bayes, Logistic Regression, MLP and RBF. also using K-fold with K = 5 Architectures used: RBF and MLP Tests using: ANOVA and Tukley test

Using the ES algorithm to train RBF-network and implement regression and classification algorithms on the dataset

EEG signal classification using Neural Networks, RBF, and genetic algorithm

Design and implementation of a simple RBF Neural Network from scratch

Classify a dataset using five different classifiers including k-NN, Support Vector Machine (with RBF kernel), Naïve Bayes, Decision Trees and Neural Networks. The objective is to experiment with parameter selection in training classifiers and to compare the performance of these well- known classification methods.

Basic implementation of FNN and RBF neural networks using tensorflow.

Implemented and compared the performance of below Classifiers using Cross-validation & error metrics. Linear classifier, K-nearest neighbor classifier, RBF neural network,1 & 2-hidden layer Neural Network

evolutionary-based approach in RBF neural network training

Generalized Improved Second Order RBF Neural Network with Center Selection using OLS

RBF network implementation as a part of Technical Neural Networks course at University of Bonn

object recognition system that can pick out and identify objects from an inputted camera image, It works on five objects cat, laptop, car, apple and Helicopter, It uses PCA for features extraction and two different neural network architectures 1- Multilayer Perceptron (MLP) with Back-Propagation learning algorithm and 2- Radial-Basis Function (RBF) with Least Mean Square learning algorithm

Predictive Estimation of Model Fidelity (PEMF) is a model-independent approach to measure the fidelity of surrogate models or metamodels, such as Kriging, Radial Basis Functions (RBF), Support Vector Regression (SVR), and Neural Networks. It can be perceived as a novel sequential and predictive implementation of K-fold cross-validation. PEMF takes as input a model trainer (e.g., RBF-multiquadric or Kriging-Linear), sample data on which to train the model, and hyper-parameter values (e.g., shape factor in RBF) to apply to the model. As output, it provides a predicted estimate of the median and/or the maximum error in the surrogate model. PEMF has been reported to be more accurate and robust than typical leave-one-out cross-validation, in providing surrogate model error measures (for various benchmark functions). The current version of PEMF has been implemented with RBF (included in this package), Kriging (DACE package), and SVR (Libsvm package), PEMF (has been and) can be readily used for the following purposes: 1. Surrogate model validation 2. Surrogate model uncertainty analysis 3. Surrogate model selection 4. Surrogate-based optimization (to guide sequential sampling) Other perceived broader applications of PEMF include testing of machine learning models and uncertainty analysis with data-driven models (and other areas where leave-one-out or k-fold cross-validation is typically used).

No description available