Showing 1-25 of 15,080 resultsfor "Index Terms":Backpropagation algorithms
"Index Terms":Backpropagation algorithms
- Sort
Get notified when new research is published matching your search criteria.
Sort By
Results
Study of PSO and Firefly algorithm based Feed-forward neural network training algorithms
2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)
Year: 2020 | Conference Paper |
Cited by: Papers (7)
Over the past few years, engineered neural network training algorithms based on nature-inspired algorithms have demonstrated their efficacy in proving their dominance over many traditional algorithms. Using the population-based approach has significantly increased the accuracy of the neural artificial network and converged towards higher accuracy. These approaches are well known to solve various p...Show More
Effect of Hyperparameters on Backpropagation
2021 IEEE Pune Section International Conference (PuneCon)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
In machine learning algorithms, parameters and hy-perparameters are important properties in the training process. Parameters are modified through machine learning algorithms while hyperparameters are the parameters that are adjusted manually to achieve the desired accuracy and increase efficiency. In neural networks, weights are parameters while hyper-parameters include layer size, momentum, learn...Show More
Comparison of four learning algorithms for multilayer perceptron with FIR synapses
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
Year: 1994 | Conference Paper |
Cited by: Papers (9)
In recent years many training algorithms for dynamic neural networks have been proposed. As a matter of fact, it is well known that the exact training algorithm for dynamic networks, is non causal and can be implemented only in batch mode. In this paper we present a comparison of three online training algorithms for dynamic networks, where each synapsis is modelled by an FIR filter. In order to ev...Show More
Genetic Algorithms for MLP Neural Network parameters optimization
2009 Chinese Control and Decision Conference
Year: 2009 | Conference Paper |
In this paper, a hybrid learning algorithm for a multilayer perceptrons (MLP) neural network using genetic algorithms (GA) is proposed. This hybrid learning algorithm has two steps: First, all the parameters (weights and biases) of the initial neural network are encoded to form a long chromosome and tuned by the GA. Second, as a result of the GA process, a quasi-Newton method called Broyden-Fletch...Show More
A Implementation for Distributed Backpropagation Using Corba Architecture
2006 5th IEEE International Conference on Cognitive Informatics
Year: 2006 | Conference Paper |
Cited by: Papers (3)
Learning plays an important role in neural computing, but it takes long time when the input data set is large and complex. Many papers have proposed how to implement learning algorithms on parallel machines or a cluster of computers to reduce learning time in the past. In this article, we present a distributed backpropagation learning that distributes the data set to learn in a cluster of computer...Show More
Training a neural observer using a hybrid approach
International Multi-Conference on Systems, Signals & Devices
Year: 2012 | Conference Paper |
In this work, we use the approach based on observers such as the neural observer in order to introduce the diagnosis of nonlinear systems. There are different techniques for training the neural networks. Among these techniques, we quote the backpropagation technique, the backpropagation technique with momentum and the hybrid one which is a mixture between the backpropagation technique and the slid...Show More
Rainfall Prediction using Neural Network
2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
About 70% of the economic sector of India depends on agriculture. Rainfall prediction plays an important role in fields like agricultural sector, fisheries, aviation, irrigation etc. Typically, in Anand (Gujarat) India, the advent of monsoon starts from June month and it continues till September month. In this work, multilayered neural network with Back-propagation learning algorithm is used. We h...Show More
Using particle swarm optimization in training neural network for indoor field strength prediction
2009 International Symposium ELMAR
Year: 2009 | Conference Paper |
This paper presents a comparison of results obtained from neural network training by backpropagation and particle swarm optimization (PSO) algorithms. The neural network model has been developed for field strength prediction in indoor environments. It has been already shown for neural networks as powerful tool in RF propagation prediction. It is very important to choose proper algorithm for traini...Show More
Prediction of Diabetes Using Data Mining Techniques
2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI)
Year: 2018 | Conference Paper |
Cited by: Papers (45)
Diabetes mellitus is fourth most high mortality rate diseases in the world and it is also a cause of kidney disease, blindness, and heart diseases. Data mining techniques support a medical decision for a correct diagnosis, treatment of disease in such way it minimizes the workload of specialists. This study proposed to predict diabetes using data mining techniques. Back propagation algorithm is us...Show More
Bayesian Bidirectional Backpropagation Learning
2021 International Joint Conference on Neural Networks (IJCNN)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
We show that training neural classifiers with Bayesian bidirectional backpropagation improves the performance of the network. Bidirectional backpropagation trains a deep network for both forward and backward recall through the same layers of neurons and with the same weights. It maximizes the network's joint forward and backward likelihood. Bayesian bidirectional backpropagation combines prior pro...Show More
An optimization method for neural network based on GA and TS algorithm
2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE)
Year: 2010 | Conference Paper |
Cited by: Papers (1)
Although many global optimization search algorithms may be used to train feedforward neural networks, these algorithms have some weaknesses such as dependence of initial solution. This paper proposes a novel hybrid global optimization method for classification problem, called GTA, which combines the advantages of Genetic algorithm and Tabu search. The training process in proposed method is divided...Show More
Accelerating the Multilayer Perceptron Learning with the Davidon Fletcher Powell Algorithm
The 2006 IEEE International Joint Conference on Neural Network Proceedings
Year: 2006 | Conference Paper |
Cited by: Papers (6)
In this paper, the Davidon Fletcher Powell (DFP) algorithm for nonlinear least squares is proposed to train multilayer perceptron (MLP). Applied on both a single output layer perceptron and MLP, we find that this algorithm is faster than the Marquardt-Levenberg (ML) algorithm known as the fastest algorithm used to train MLP until now. The number of iterations required by DFP algorithm to converge ...Show More
Bank Indonesia Interest Rate Prediction and Forecast With Backpropagation Neural Network
2018 International Conference on Information Technology Systems and Innovation (ICITSI)
Year: 2018 | Conference Paper |
Cited by: Papers (5)
The BI Rate is a policy interest rate that plays a role in directing the movement of the national economy. The problem that arises in the study is to determine a forecast for the movement of the BI Rate. Predictions of bank interest rates can be done with various techniques and methods, one of which uses backpropagation artificial neural networks. This method is a branch of artificial intelligence...Show More
Mass Appraisal of Real Estate Prices Using Improved BP Neural Network with Policy Evaluation
2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
This paper proposes a new policy index neural network (NN), which uses the back propagation (BP) NN model optimized by genetic algorithm (GA) and particle swarm algorithm and the policy modeling consistency (PMC) index to quantify the relevant real estate policies. This study also trains and validates the big data of primary housing transactions of 33 properties in the main urban area of Weihai Ci...Show More
Backpropagation learning method with interval type-2 fuzzy weights in neural networks
Fernando Gaxiola;Patricia Melin;Fevrier Valdez;Oscar Castillo
The 2013 International Joint Conference on Neural Networks (IJCNN)
Year: 2013 | Conference Paper |
Cited by: Papers (2)
In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this pape...Show More
Forecasting the Inflation Rate in Indonesia Using Backpropagation Artificial Neural Network
Agung Mustika Rizki;Gusti Eka Yuliastuti;Afina Lina Nurlaili;Firza Prima Aditiawan
2022 IEEE 8th Information Technology International Seminar (ITIS)
Year: 2022 | Conference Paper |
Economic growth is an essential indicator in measuring a country’s economic development. The stability of the Rupiah exchange rate against the prices of goods and services is included in inflation. Another problem is an increase in the poverty rate. This happens because inflation tends to rise while people’s real income continues to decline, so people’s living standards also follow. This inflation...Show More
Analyzing the Characteristics of Gradient Descent and Non-Gradient Descent-Based Algorithms in Neural Network Learning
2024 International Conference on Electrical, Computer and Energy Technologies (ICECET
Year: 2024 | Conference Paper |
In this paper, a comparative analysis of optimization algorithms for artificial neural networks widely used in the fields of machine learning and deep learning was conducted. Beyond the conventional backpropagation algorithm utilizing gradient descent, we investigated the characteristics of various optimization methods for neural networks, including alternative non-gradient descent approaches such...Show More
Comparison of different backpropagation training algorithms using robust M-estimators performance functions
2015 Tenth International Conference on Computer Engineering & Systems (ICCES)
Year: 2015 | Conference Paper |
Cited by: Papers (11)
Artificial neural networks are one of the most popular and promising areas of artificial intelligence research. Training data containing outliers are often a problem for supervised neural networks learning algorithms that may not always come up with acceptable performance. Many robust learning algorithms have been proposed so far to improve the performance of neural networks in the presence of out...Show More
Feed-Forward Neural Network for Graphical Symbol Recognition in Additive Noise Environment
2021 Big Data, Knowledge and Control Systems Engineering (BdKCSE)
Year: 2021 | Conference Paper |
This article proposes a feed-forward backpropagation neural network architecture for the automatic recognition of graphical symbols in the presence of high-level additive Gaussian noise based on machine learning (ML). The article presents the comparison of two different backpropagation training algorithms and presents numerical results which compare the performances of a Levenberg-Marquardt backpr...Show More
Fast training of multilayer perceptrons with a mixed norm algorithm
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.
Year: 2005 | Conference Paper |
Cited by: Papers (4)
A new fast training algorithm for the multilayer perceptron (MLP) is proposed. This new algorithm is based on the optimization of a mixed least square (LS) and a least fourth (LF) criterion producing a modified form of the standard back propagation algorithm (SBP). To determine the updating rules in the hidden layers, an analogous back propagation strategy used in the conventional learning algorit...Show More
Evolutionary approach for approximation of artificial neural network
2010 IEEE 2nd International Advance Computing Conference (IACC)
Year: 2010 | Conference Paper |
Cited by: Papers (2)
Neural Network is an effective tool in the field of pattern recognition. The neural network classifies the pattern from the training data and recognizes if the testing data holds that pattern. The classical Back propagation (BP) algorithm is generally used to train the neural network for its simplicity. The basic drawback of this algorithm is its uncertainty and long training time and it searches ...
Show More
First Page of the Article
Show MoreHybrid learning algorithm with low input-to-output mapping sensitivity for iterated time-series prediction
Proceedings of International Conference on Neural Networks (ICNN'97)
Year: 1997 | Conference Paper |
A hybrid backpropagation/Hebbian learning rule had been developed to enforce low input-to-output mapping sensitivities for feedforward neural networks. This additional functionality is coded as additional weak constraints into the cost function. For numerical efficiency and easy interpretations we specifically designed the additional cost terms with the first order derivatives at hidden-layer neur...Show More
A Novel Learning Algorithm of Back-Propagation Neural Network
2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)
Year: 2009 | Conference Paper |
Cited by: Papers (4)
Standard neural network based on back-propagation learning algorithm has some faults, such as low learning rate, instability, and long learning time. In this paper, we introduce trust-field method and bring forward a new learning factor, meanwhile we adopt Quasic-Newton algorithm to replace gradient descent algorithm. Three algorithms are utilized in the novel back-propagation neural network. Thus...Show More
Application of Artificial Neural Network to Predict Short-Term Capital Flow
2009 International Conference on Research Challenges in Computer Science
Year: 2009 | Conference Paper |
Cited by: Papers (1)
The last decade witnessed a significant increase in net private capital inflows in China. Some of them are short-term capital flows, which are typically considered to be highly volatile. For effectively forecasting the short-term capital flows, a three-layered neural feedforward network was employed in this paper. In light of the weakness of the conventional Back-Propagation algorithm, the Levenbe...Show More
Traffic Flow Forecasting Algorithm Using Simulated Annealing Genetic BP Network
2010 International Conference on Measuring Technology and Mechatronics Automation
Year: 2010 | Conference Paper |
Cited by: Papers (8)
Genetic back propagation (BP) neural network is fast, quick, steady in forecasting of traffic flow, and the result has lowly error ability. But it can easily cause premature convergence, and usually the solution we got is local optimal solution. For overcoming those drawbacks of Genetic BP neural network, we add Simulated Annealing Algorithm to the processing of GA, using the ability of Annealing ...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.