Comparison of gradient descent method, Kalman filtering and decoupled kalman in training neural networks used for fingerprint-based positioning

2005 ◽  
Author(s):  
C.M. Takenga ◽  
K. Rao Anne ◽  
K. Kyamakya ◽  
J. Chamberlain Chedjou
Keyword(s):  
Neural Networks ◽  
Kalman Filtering ◽  
Gradient Descent ◽  
Descent Method ◽  
Gradient Descent Method

scholarly journals SOME METHODS OF ADAPTIVE MULTILAYER NEURAL NETWORKS TRAINING

2014 ◽  
pp. 99-106
Author(s):  
Leonid Makhnist ◽  
Nikolaj Maniakov ◽  
Nikolaj Maniakov
Keyword(s):  
Neural Networks ◽  
Basic Concept ◽  
Gradient Descent ◽  
Descent Method ◽  
Gradient Descent Method ◽  
New Techniques ◽  
Adaptive Training ◽  
Multilayer Neural Networks

Is proposed two new techniques for multilayer neural networks training. Its basic concept is based on the gradient descent method. For every methodic are showed formulas for calculation of the adaptive training steps. Presented matrix algorithmizations for all of these techniques are very helpful in its program realization.

scholarly journals Adaptive Natural Gradient Method for Learning of Stochastic Neural Networks in Mini-Batch Mode

2019 ◽  
Vol 9 (21) ◽  
pp. 4568
Author(s):  
Hyeyoung Park ◽  
Kwanyong Lee
Keyword(s):  
Neural Networks ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Descent Method ◽  
Benchmark Problems ◽  
Stochastic Neural Networks ◽  
Gradient Descent Method ◽  
Natural Gradient ◽  
Convergence Properties ◽  
Data Set

Gradient descent method is an essential algorithm for learning of neural networks. Among diverse variations of gradient descent method that have been developed for accelerating learning speed, the natural gradient learning is based on the theory of information geometry on stochastic neuromanifold, and is known to have ideal convergence properties. Despite its theoretical advantages, the pure natural gradient has some limitations that prevent its practical usage. In order to get the explicit value of the natural gradient, it is required to know true probability distribution of input variables, and to calculate inverse of a matrix with the square size of the number of parameters. Though an adaptive estimation of the natural gradient has been proposed as a solution, it was originally developed for online learning mode, which is computationally inefficient for the learning of large data set. In this paper, we propose a novel adaptive natural gradient estimation for mini-batch learning mode, which is commonly adopted for big data analysis. For two representative stochastic neural network models, we present explicit rules of parameter updates and learning algorithm. Through experiments on three benchmark problems, we confirm that the proposed method has superior convergence properties to the conventional methods.

Globally convergent stochastic optimization with optimal asymptotic distribution

1998 ◽  
Vol 35 (02) ◽  
pp. 395-406 ◽  
Author(s):  
Jürgen Dippon
Keyword(s):  
Neural Networks ◽  
Stochastic Optimization ◽  
Asymptotic Distribution ◽  
Gradient Descent ◽  
Likelihood Estimation ◽  
Descent Method ◽  
Stochastic Gradient Descent ◽  
Gradient Descent Method ◽  
Optimal Convergence Rate ◽  
Globally Convergent

A stochastic gradient descent method is combined with a consistent auxiliary estimate to achieve global convergence of the recursion. Using step lengths converging to zero slower than 1/n and averaging the trajectories, yields the optimal convergence rate of 1/√n and the optimal variance of the asymptotic distribution. Possible applications can be found in maximum likelihood estimation, regression analysis, training of artificial neural networks, and stochastic optimization.

scholarly journals IDENTIFICATION OF AREAS OF CORONAVIRUS COVID-19 INCIDENCE SPREADING BASED ON CLUSTER ANALYSIS METHOD

2021 ◽  
pp. 5-13
Author(s):  
Kseniia Bazilevych ◽  
Ievgen Meniailov ◽  
Dmytro Chumachenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Gradient Descent ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Adaptive Boosting ◽  
Software Product ◽  
Boosting Method

Subject: the use of the mathematical apparatus of neural networks for the scientific substantiation of anti-epidemic measures in order to reduce the incidence of diseases when making effective management decisions. Purpose: to apply cluster analysis, based on a neural network, to solve the problem of identifying areas of incidence. Tasks: to analyze methods of data analysis to solve the clustering problem; to develop a neural network method for clustering the territory of Ukraine according to the nature of the epidemic process COVID-19; on the basis of the developed method, to implement a data analysis software product to identify the areas of incidence of the disease using the example of the coronavirus COVID-19. Methods: models and methods of data analysis, models and methods of systems theory (based on the information approach), machine learning methods, in particular the Adaptive Boosting method (based on the gradient descent method), methods for training neural networks. Results: we used the data of the Center for Public Health of the Ministry of Health of Ukraine distributed over the regions of Ukraine on the incidence of COVID-19, the number of laboratory examined persons, the number of laboratory tests performed by PCR and ELISA methods, the number of laboratory tests of IgA, IgM, IgG; the model used data from March 2020 to December 2020, the modeling did not take into account data from the temporarily occupied territories of Ukraine; for cluster analysis, a neural network of 60 input neurons, 100 hidden neurons with an activation Fermi function and 4 output neurons was built; for the software implementation of the model, the programming language Python was used. Conclusions: analysis of methods for constructing neural networks; analysis of training methods for neural networks, including the use of the gradient descent method for the Adaptive Boosting method; all theoretical information described in this work was used to implement a software product for processing test data for COVID-19 in Ukraine; the division of the regions of Ukraine into zones of infection with the COVID-19 virus was carried out and a map of this division was presented.

Globally convergent stochastic optimization with optimal asymptotic distribution

1998 ◽  
Vol 35 (2) ◽  
pp. 395-406 ◽  
Author(s):  
Jürgen Dippon
Keyword(s):  
Neural Networks ◽  
Stochastic Optimization ◽  
Asymptotic Distribution ◽  
Gradient Descent ◽  
Likelihood Estimation ◽  
Descent Method ◽  
Stochastic Gradient Descent ◽  
Gradient Descent Method ◽  
Optimal Convergence Rate ◽  
Globally Convergent

A stochastic gradient descent method is combined with a consistent auxiliary estimate to achieve global convergence of the recursion. Using step lengths converging to zero slower than 1/n and averaging the trajectories, yields the optimal convergence rate of 1/√n and the optimal variance of the asymptotic distribution. Possible applications can be found in maximum likelihood estimation, regression analysis, training of artificial neural networks, and stochastic optimization.

An improved Mamdani Fuzzy Neural Networks Based on PSO Algorithm and New Parameter Optimization

2016 ◽  
Vol 1 (1) ◽  
pp. 201 ◽  
Author(s):  
Lei Meng ◽  
Shoulin Yin ◽  
Xinyuan Hu
Keyword(s):  
Neural Networks ◽  
Parameter Optimization ◽  
Gradient Descent ◽  
Fuzzy Rule ◽  
Pso Algorithm ◽  
Fuzzy Neural Networks ◽  
Descent Method ◽  
Local Optimum ◽  
Gradient Descent Method ◽  
Fuzzy Neural

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.

THE BACKPROPAGATION ALGORITHM IN J, A FAST PROTOTYPING TOOL FOR RESEARCHING NEURAL NETWORKS

1999 ◽  
Vol 09 (04) ◽  
pp. 273-284 ◽  
Author(s):  
ROELOF K. BROUWER
Keyword(s):  
Neural Networks ◽  
Gradient Descent ◽  
Back Propagation ◽  
General Purpose ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Back Propagation Algorithm ◽  
Software Packages ◽  
Fast Prototyping ◽  
Network Simulators

This paper illustrates the use of a powerful language, called J, that is ideal for simulating neural networks. The use of J is demonstrated by its application to a gradient descent method for training a multilayer perceptron. It is also shown how the back-propagation algorithm can be easily generalized to multilayer networks without any increase in complexity and that the algorithm can be completely expressed in an array notation which is directly executable through J. J is a general purpose language, which means that its user is given a flexibility not available in neural network simulators or in software packages such as MATLAB. Yet, because of its numerous operators, J allows a very succinct code to be used, leading to a tremendous decrease in development time.

scholarly journals Fractional-Order Deep Backpropagation Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Chunhui Bao ◽  
Yifei Pu ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fractional Order ◽  
Bp Neural Network ◽  
Gradient Descent ◽  
Necessary Conditions ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Backpropagation Neural Network ◽  
Bp Neural Network Model

In recent years, the research of artificial neural networks based on fractional calculus has attracted much attention. In this paper, we proposed a fractional-order deep backpropagation (BP) neural network model with L2 regularization. The proposed network was optimized by the fractional gradient descent method with Caputo derivative. We also illustrated the necessary conditions for the convergence of the proposed network. The influence of L2 regularization on the convergence was analyzed with the fractional-order variational method. The experiments have been performed on the MNIST dataset to demonstrate that the proposed network was deterministically convergent and can effectively avoid overfitting.

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