scholarly journals Development Multiple Neuro-Fuzzy System Using Back-propagation Algorithm

2013 ◽  
Vol 6 (2) ◽  
pp. 794-804
Author(s):  
Dr. Imad S. Alshawi ◽  
Haider Khalaf Allamy ◽  
Dr. Rafiqul Zaman Khan
Keyword(s):  
Fuzzy System ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Back Propagation Algorithm ◽  
New Approach ◽  
Training Time ◽  
Propagation Algorithm ◽  
Neuro Fuzzy ◽  
Highly Nonlinear ◽  
Input Variables

When fuzzy systems are highly nonlinear or include a large number of input variables, the number of fuzzy rules constituting the underlying model is usually large. Dealing with a large-size fuzzy model may face many practical problems in terms of training time, ease of updating, generalizing ability and interpretability. Multiple Fuzzy System (MFS) is one of effective methods to reduce the number of rules, increase the speed to obtain good results. This paper is therefore proposes another approach call Multiple Neuro-Fuzzy System (MNFS) which can further enhance the performance of the MFS approach. The new approach is used Back-propagation algorithm in the learning process. The performance of the proposed approach evaluates and compares with MFS by three experiments on nonlinear functions. Simulation results demonstrate the effectiveness of the new approach than MFS with regards to enhancement of the accuracy of the results.  

A Neural Network Model of Students’ English Abilities Based on Their Affective Factors in Learning

2012 ◽  
Vol 16 (3) ◽  
pp. 375-380 ◽  
Author(s):  
Fitra A. Bachtiar ◽  
◽  
Katsuari Kamei ◽  
Eric W. Cooper ◽  
Keyword(s):  
Teaching And Learning ◽  
Back Propagation ◽  
Model Estimation ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
New Approach ◽  
Affective Factors ◽  
Teaching Perspectives ◽  
Propagation Algorithm ◽  
High Level

The gap between teaching perspectives and students’ differences may impact negatively on teaching and learning effectiveness, indicating the need for a new approach for bridging this gap. The potentials of artificial neural networks for approximating extremely complex problems encouraged us to develop an estimation model of student English ability. The model was trained using a back propagation algorithm and tested using 154 samples from two universities. The model estimation rate related to student English ability demonstrated a high level of estimation by 93.34% for listening, 94.38% for reading, 94.90% for speaking, and 93.58% for writing.

scholarly journals Two-Stage Water Jet Landing Point Prediction Model for Intelligent Water Shooting Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2704
Author(s):  
Yunhan Lin ◽  
Wenlong Ji ◽  
Haowei He ◽  
Yaojie Chen
Keyword(s):  
Pitch Angle ◽  
Back Propagation ◽  
Water Jet ◽  
The State ◽  
Model Parameters ◽  
Propagation Time ◽  
Back Propagation Algorithm ◽  
Angle Error ◽  
Landing Point ◽  
Propagation Algorithm

In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fit a model that satisfies the law of jet motion in ideal conditions without interference. In the second stage, the model uses the back-propagation algorithm to update the parameters online according to the visual feedback of the landing point position. The model established by this method can dynamically eliminate the interference of external factors and realize precise on-target shooting. The simulation results show that the model can dynamically adjust the parameters according to the state relationship between the landing point and the desired target, which keeps the predicted pitch angle error within 0.1°. In the test on the actual platform, when the landing point is 0.5 m away from the position of the desired target, the model only needs 0.3 s to adjust the water jet to hit the target. Compared to the state-of-the-art method, GA-BP (genetic algorithm-back-propagation), the proposed method’s predicted pitch angle error is within 0.1 degree with 1/4 model parameters, while costing 1/7 forward propagation time and 1/200 back-propagation calculation time.

Studying Fitting Effect by ANFIS in the Electrodialysis Process

2011 ◽  
Vol 268-270 ◽  
pp. 336-339
Author(s):  
Guo Lin Jing ◽  
Wen Ting Du ◽  
Quan Zhou ◽  
Song Tao Li
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Least Square Method ◽  
Least Square ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Error Back Propagation ◽  
Error Back Propagation Algorithm

Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to study fitting effect by ANFIS in a laboratory scale ED cell. Separation percent of NaCl solution is mainly as a function of concentration, temperature, flow rate and voltage. Besides, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically, using the error back propagation algorithm and least square method to adjust the parameters of fuzzy inference system. We obtained fitted values of separation percent by ANFIS. Separation percent from experiments compared with the fitted values of separation percent. The result is shown that the correlation coefficient is 0.988. Therefore, it is verified as a good performance in the electrodialysis process.

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

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