Wilt Dataset-based Comparative Analysis of Three Neural Networks

2020 ◽  
Author(s):  
Ion Panfilii ◽  
Radu-Emil Precup ◽  
Raul-Cristian Roman ◽  
Emil M. Petriu
Keyword(s):  
Neural Networks ◽  
Comparative Analysis

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

scholarly journals Comparative analysis of the geometrized histogram method and the neural network method for road markings recognition

2021 ◽  
pp. 1-22
Author(s):  
Aleksei Valerievich Podoprosvetov ◽  
Dmitry Anatolevich Anokhin ◽  
Konstantin Ivanovich Kiy ◽  
Igor Aleksandrovich Orlov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Computational Complexity ◽  
Video Sequences ◽  
Histogram Method ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Accuracy Speed

This paper compares two approaches to determining road markings from video sequences, namely, the method of finding the markings using geometrized histograms and the method based on neural networks. An independent open dataset TuSimple is used to conduct a comparative analysis of the algorithms. Since the investigated methods have different architectures, their work is evaluated according to the following metrics: Accuracy, speed (relative FPS), general computational complexity of the algorithm (TFlops).

scholarly journals COMPARATIVE ANALYSIS OF APPROXIMATION METHODS USING REGRESSION RELATIONSHIPS AND NEURAL NETWORKS FOR LINEAR MODELS

2021 ◽  
Vol 2021 (1) ◽  
pp. 31-35
Author(s):  
Svetlana Senotova
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Linear Models ◽  
Approximation Methods

The paper examines comparative analysis of approximation methods using regression dependencies and neural networks for linear models.

Comparative analysis of activation functions in neural networks

2021 ◽  
Author(s):  
Firuz Kamalov ◽  
Amril Nazir ◽  
Murodbek Safaraliev ◽  
Aswani Kumar Cherukuri ◽  
Rita Zgheib
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Activation Functions
Sign in / Sign up

Export Citation Format

Share Document