The Neural Network Algorithm for Data-Mining in Dynamic Environments

2008 ◽  
Author(s):  
Ching Lien Huang ◽  
Tsung-Shin Hsu ◽  
Chih-Ming Liu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Dynamic Environments ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

scholarly journals Neural Network Model for Mathematic Scores Prediction: Case Study in SMK Negeri Pakis Aji, Jepara, Indonesia

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Adi Sucipto ◽  
Joko Minardi
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Mean Square ◽  
Mathematics Scores ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Conducting Research ◽  
Using Data

Aim of this research is to apply Neural Network Algorithm to predict score of mathematic in the national exam. During the time, the teacher only provided national exam materials and additional tryout tests without knowing how to predict the exam scores in mathematics subject. Data mining neural network algorithm obtained \Root Mean Square Error (RMSE) values which were used as basic improvement and clustering class By conducting research using data mining neural network algorithm, it proved that this model can be used to predict scores of Mathematics subject at SMK Negeri 1 Pakis Aji.. The result of this research by using data mining neural network algorithm found RMSE 0138 +/- 0.092. The lower the RMSE values the more accurate the neural network to predict mathematics scores of SMK Negeri 1 Pakis Aji.Received: 18 Agustus 2019; Accepted: 5 Januari 2020; Published: 14 January 2020

scholarly journals Prediction Modelling of Cold Chain Logistics Demand Based on Data Mining Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bo He ◽  
Lvjiang Yin
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Cold Chain ◽  
Data Mining Algorithm ◽  
Positive Effects ◽  
Network Algorithm ◽  
The Neural Network ◽  
First Choice ◽  
Neural Network Algorithm ◽  
Logistics Demand

Modern information technologies such as big data and cloud computing are increasingly important and widely applied in engineering and management. In terms of cold chain logistics, data mining also exerts positive effects on it. Specifically, accurate prediction of cold chain logistics demand is conducive to optimizing management processes as well as improving management efficiency, which is the main purpose of this research. In this paper, we analyze the existing problems related to cold chain logistics in the context of Chinese market, especially the aspect of demand prediction. Then, we conduct the mathematical calculation based on the neural network algorithm and grey prediction. Two forecasting models are constructed with the data from 2013 to 2019 by R program 4.0.2, aiming to explore the cold chain logistics demand. According to the results estimated by the two models, we find that both of models show high accuracy. In particular, the prediction of neural network algorithm model is closer to the actual value with smaller errors. Therefore, it is better to consider the neural network algorithm as the first choice when constructing the mathematical forecasting model to predict the demand of cold chain logistic, which provides a more accurate reference for the strategic deployment of logistics management such as optimizing automation and innovation in cold chain processes to adapt to the trend.

scholarly journals WRF-Chem Simulation of Winter Visibility in Jiangsu, China, and the Application of a Neural Network Algorithm

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 520
Author(s):  
Peishu Zong ◽  
Yali Zhu ◽  
Huijun Wang ◽  
Duanyang Liu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Extinction Coefficient ◽  
Wrf Model ◽  
Jiangsu Province ◽  
Network Algorithm ◽  
The Neural Network ◽  
Noticeable Improvement ◽  
Neural Network Algorithm ◽  
Meteorological Elements

In this paper, the winter visibility in Jiangsu Province is simulated by WRF-Chem (Weather Research and Forecasting (WRF) model coupled with Chemistry) with high spatiotemporal resolutions. Simulation results show that WRF-Chem has good capability to simulate the visibility and related local meteorological elements and air pollutants in Jiangsu in the winters of 2013–2017. For visibility inversion, this study adopts the neural network algorithm. Meteorological elements, including wind speed, humidity and temperature, are introduced to improve the performance of WRF-Chem relative to the visibility inversion scheme, which is based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) extinction coefficient algorithm. The neural network offers a noticeable improvement relative to the inversion scheme of the IMPROVE visibility extinction coefficient, substantially improving the underestimation of winter visibility in Jiangsu Province. For instance, the correlation coefficient increased from 0.17 to 0.42, and root mean square error decreased from 2.62 to 1.76. The visibility inversion results under different humidity and wind speed levels show that the underestimation of the visibility using the IMPROVE scheme is especially remarkable. However, the underestimation issue is essentially solved using the neural network algorithm. This study serves as a basis for further predicting winter haze events in Jiangsu Province using WRF-Chem and deep-learning methods.

Neural Network Algorithm for Solving Large Scale Travelling Salesman Problems

2012 ◽  
Vol 542-543 ◽  
pp. 1398-1402
Author(s):  
Guo Zhong Cheng ◽  
Wei Feng ◽  
Fang Song Cui ◽  
Shi Lu Zhang
Keyword(s):  
Neural Network ◽  
Time Complexity ◽  
Large Scale ◽  
Travelling Salesman Problem ◽  
Effective Algorithm ◽  
Travelling Salesman ◽  
Satisfactory Solution ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

This study improves the neural network algorithm that was presented by J.J.Hopfield for solving TSP(travelling salesman problem) and gets an effective algorithm whose time complexity is O(n*n), so we can solve quickly TSP more than 500 cities in microcomputer. The paper considers the algorithm based on the replacement function of the V Value. The improved algorithm can greatly reduces the time and space complexities of Hopfield method. The TSP examples show that the proposed algorithm could efficiently find a satisfactory solution and has a fast convergence speed.

Data Mining with BP Neural Network Algorithm Based MapReduce

2013 ◽  
Vol 380-384 ◽  
pp. 2915-2919 ◽  
Author(s):  
Jian Ming Cui ◽  
Yan Xin Ye
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Resource Constraints ◽  
Training Time ◽  
Computing Platform ◽  
Network Training ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Cloud Computing Platform

Traditional massive data mining with BP neural network algorithm, resource constraints of the ordinary stand-alone platform and scalability bottlenecks and classification process serialization due to classification inefficient results, and also have an impact on the classification accuracy. In this paper, the Detailed description of the flow of execution of the BP neural network parallel algorithm in Hadoop's MapReduce programming model.Experimental results show that: the BP neural network under the cloud computing platform can greatly shorten the network training time, better parallel efficiency and good scalability.

Health Monitoring: New Techniques Based on Vibrations Measurements and Identification Algorithms on an Aeronautical Composite Panel

2005 ◽  
Vol 293-294 ◽  
pp. 575-582 ◽  
Author(s):  
Igor Bovio ◽  
M. Della Ragione ◽  
Leonardo Lecce
Keyword(s):  
Neural Network ◽  
Health Monitoring ◽  
Structural Damage ◽  
Environmental Parameters ◽  
Composite Panel ◽  
Maintenance Cost ◽  
New Techniques ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

Purpose of the paper is to present a new application of a Non Destructive Test based on vibrations measurements, developed by the authors and already tested for analysing damage of many structural elements. The proposed new method is based on the acquisition and comparison of Frequency Response Functions (FRFs) of the monitored structure before and after an occurred damage. Structural damage modify the dynamical behaviour of the structure such as mass, stiffened and damping, and consequently its FRFs, making possible to identify and quantify a structural damage. The activities, presented in the paper, mostly focused on a new FRFs processing technique based on a dedicated neural network algorithm aimed at obtaining a “recognition-based learning”; this kind of learning methodology permits to train the neural network in order to let it recognise only “positive” examples discarding, as a consequence, the “negative” ones. Within the structural NDT a “positive” example means “healthy” state of the analysed structural component and, obviously, a “negative” one means a “damaged” or perturbed state. The developed NDT has been tested for identifying and analysing damage on an aeronautical composite panel to validate the method and calibrate the neural network algorithm. These tests have permitted to understand the influence of environmental parameters on the neural network training capability. Thanks to these new techniques it is possible to carry out a smart Health Monitoring system which is going to lead to the reduction of time and maintenance cost and to the increase of the aeronautical structure safety and reliability.

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