An Adaptive Learning Method in Automated Negotiation Based on Artificial Neural Network

2005 ◽  
Author(s):  
Zi-Ming Zeng ◽  
Bo Meng ◽  
Yuan-Yuan Zeng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Automated Negotiation ◽  
Learning Method ◽  
Artificial Neural

A Zero-Shot Learning Method Using Artificial Neural Network for Drift Calibration of Gas Sensor Array

2020 ◽  
Vol MA2020-01 (26) ◽  
pp. 1856-1856
Author(s):  
Yu-Chieh Cheng ◽  
Ting-I Chou ◽  
Jye-Luen Lee ◽  
Shih-Wen Chiu ◽  
Kea Tiong Tang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Sensor ◽  
Sensor Array ◽  
Learning Method ◽  
Gas Sensor Array ◽  
Artificial Neural

Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network

2017 ◽  
Vol 68 (11) ◽  
pp. 2070 ◽  
Author(s):  
Manh-Ha Bui ◽  
Thanh-Luu Pham ◽  
Thanh-Son Dao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Transfer Functions ◽  
Learning Algorithm ◽  
Oxygen Demand ◽  
Cyanobacterial Blooms ◽  
Cyanobacteria Bloom ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

An artificial neural network (ANN) model was used to predict the cyanobacteria bloom in the Dau Tieng Reservoir, Vietnam. Eight environmental parameters (pH, dissolved oxygen, temperature, total dissolved solids, total nitrogen (TN), total phosphorus, biochemical oxygen demand and chemical oxygen demand) were introduced as inputs, whereas the cell density of three cyanobacteria genera (Anabaena, Microcystis and Oscillatoria) with microcystin concentrations were introduced as outputs of the three-layer feed-forward back-propagation ANN. Eighty networks covering all combinations of four learning algorithms (Bayesian regularisation (BR), gradient descent with momentum and adaptive learning rate, Levenberg–Mardquart, scaled conjugate gradient) with two transfer functions (tansig, logsig) and 10 numbers of hidden neurons (6–16) were trained and validated to find the best configuration fitting the observed data. The result is a network using the BR learning algorithm, tansig transfer function and nine neurons in the hidden layer, which shows satisfactory predictions with the low values of error (root mean square error=0.108) and high correlation coefficient values (R=0.904) between experimental and predicted values. Sensitivity analysis on the developed ANN indicated that TN and temperature had the most positive and negative effects respectively on microcystin concentrations. These results indicate that ANN modelling can effectively predict the behaviour of the cyanobacteria bloom process.

scholarly journals Online learning method based on artificial neural network to optimize magnetic shielding characteristic parameters

2019 ◽  
Vol 68 (13) ◽  
pp. 130701
Author(s):  
Xiang-Kai Peng ◽  
Jing-Wei Ji ◽  
Lin Li ◽  
Wei Ren ◽  
Jing-Feng Xiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Learning ◽  
Magnetic Shielding ◽  
Learning Method ◽  
Characteristic Parameters ◽  
Artificial Neural

scholarly journals Recognition of Beam's Music Notation Patterns Using Artificial Neural Networks with The Backpropagation Method

2019 ◽  
Vol 3 (1) ◽  
pp. 41
Author(s):  
John Pierre Haumahu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
System Development ◽  
Musical Instruments ◽  
Music Notation ◽  
Learning Method ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Numerical Notation

<p class="8AbstrakBahasaIndonesia"><span>The beam notations is officially used as the standard of international music notation, and is often found in scores for both musical instruments and vocals. In Indonesia, the use of numerical notation is more widely used and understood, because the learning process of notation beams is not easy, and takes time for the introduction of each symbol and its meaning. The pattern recognition technology makes it possible to recognize the pattern of the beam notations. The software used for system development is Matlab, utilizing artificial neural network using backpropagation method to recognize the pattern of beam notation. Backpropagation is a supervised learning method, where the system will be given the training first, and then the system can understand and identify patterns based on the knowledge gained. The final result shows that the system is able to recognize patterns from notations that have been previously studied with the highest percentage of 91.20%.</span></p>

scholarly journals Artificial Neural Network in the CATS Training System

2019 ◽  
pp. 53-59
Author(s):  
Yu. V. Popova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Back Propagation ◽  
Main Idea ◽  
Training System ◽  
Educational Process ◽  
Academic Disciplines ◽  
Learning Trajectory ◽  
Artificial Neural

This paper presents a variant of using an artificial neural network (ANN) for adaptive learning. The main idea of using ANN is to apply it for a specific educational material, so that after completing the course or its separate topic, the student can determine, not only his level of knowledge, without the teacher’s participation, but also get some recommendations on what material needs to be studied further due to gaps in the studied issues. This approach allows you to build an individual learning trajectory, significantly reduce the time to study academic disciplines and improve the quality of the educational process. The training of an artificial neural network takes place according to the method of back propagation of an error. The developed ANN can be applied to study any academic discipline with a different number of topics and control questions. The research results are implemented and tested in the CATS adaptive training system. This system is the author's development.

Artificial Neural Network Model with PSO as a Learning Method to Predict Movement of the Rupiah Exchange Rate against the US Dollar

2021 ◽  
pp. 81
Author(s):  
Eko Verianto ◽  
Budi Sutedjo Dharma Oetomo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Pso Algorithm ◽  
Learning Method ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
The Us ◽  
Us Dollar ◽  
Artificial Neural

The movement of currency exchange rate can be predicted in the next few days, this is used by economic actors to get profit. Artificial Neural Network with the backpropagation learning method is good enough to use for forecasting time series data, it's just that in its application this method was considered to have shortcomings such as a long training time to achieve convergence. The purpose of this research is to form a Multilayer Perceptron Artificial Neural Network model with the Particle Swarm Optimization (PSO) algorithm as a learning method in the case of currency exchange rate prediction. This research produced a model that can predict the movement of the Rupiah exchange rate against the US Dollar, while the model formed was the MLP-PSO model with an error rate of 5.6168 x 10-8, slightly better than the MLP-BP model with an error rate of 6.4683 x 10-8. These results indicated that the PSO algorithm can be used as a learning algorithm in the Multilayer Perceptron Artificial Neural Network.

scholarly journals Development of an adaptive mass open online course in the framework of training in artificial neural network technologies

2021 ◽  
Vol 18 (1) ◽  
pp. 100-106
Author(s):  
Dmitry V. Bordachev
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Learning Technologies ◽  
Online Course ◽  
Artificial Neural ◽  
The Moment ◽  
High Level ◽  
Network Technologies ◽  
Neural Network Technologies

Problem and goal. The development of mass open online courses contributes to the increasing attention of students to them. At the moment, there are many large services that provide online training, but there are no clearly defined universal requirements for such courses. Also, along with this problem, there is a fairly high level of rejection of the course at various stages due to the loss of motivation to continue training. Methodology. A variant of solving these problems by using adaptive learning technologies on the example of a course on learning artificial neural network technologies was considered. Results. In the process of reviewing the issue, the topics of the online course sections were determined. As a result, a work plan was drafted and the most relevant ways to solve the identified problems were formulated. Conclusion. The developed strategy can help with further elaboration and testing of the designed course and can be applied to any mass open online course.

scholarly journals TIME SERIES FORECASTING USING ARTIFICIAL NEURAL NETWORK WITH EXTENDED ADAPTIVE LEARNING

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Mr. Dhanaji Vilas Mirajkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Learning Rate ◽  
Experimental Result ◽  
Adaptive Learning Rate ◽  
Artificial Neural

Artificial neural network (ANN) mainly consists of learning algorithms, which are require to optimize the convergence of neural networks. We need to optimize the convergence of neural networks in order to improve the speed and accuracy of decision making process. To enable the optimization process one of the widely used algorithm is back propagation learning algorithm. Objective of study is to applied backpropagation algorithm for solving multivariate time series problem. To better the accuracy of neural network it is important to find optimized architecture for the problem under consideration. The learning rate is also an important factor which affects the performance of result. In this study, we proposed extended adaptive learning approach in which learning rate is adapted from number of previous iteration error trend in first half of training. In next half of training learning rate is adapted as per adaptive learning rate algorithm. Compare performance of three variation of backpropagationalgorithm. All these variation experimented on two standard dataset. Experimental result shows that during validation and training ANN with extended adaptive learning rate outperforms other than two variations.

Sign in / Sign up

Export Citation Format

Share Document