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.

scholarly journals FPGA Accelerating Core Design Based on XNOR Neural Network Algorithm

2018 ◽  
Vol 173 ◽  
pp. 01024
Author(s):  
Su Yi ◽  
Hu Xiao ◽  
Sun Yongjie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Computational Efficiency ◽  
Large Scale ◽  
Structural Characteristics ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Computing Platforms ◽  
Fpga Acceleration

The current deep learning application scenario is more and more extensive. In terms of computing platforms, the widely used GPU platforms have lower computational efficiency. The flexibility of APU-dedicated processors is difficult to deal with evolving algorithms, and the FPGA platform takes into account both computational flexibility and computational efficiency. At present, one of the bottlenecks for limiting large-scale deep learning algorithms on FPGA platforms is the large-scale floating-point computing. Therefore, this article studies single-bit parameterized quantized neural network algorithm (XNOR), and optimizes the neural network algorithm based on the structural characteristics of the FPGA platform., Design and implementation of the FPGA acceleration core, the experimental results show that the acceleration effect is obvious.

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.

PBP: A Type of BP Neural Network Algorithm Based on Priori Triggering

2011 ◽  
Vol 271-273 ◽  
pp. 441-447
Author(s):  
Xiao Mei Chen ◽  
Dang Gang ◽  
Tian Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Large Scale ◽  
Learning Ability ◽  
Traffic Flows ◽  
It Use ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Large Scale Networks ◽  
High Level

The algorithm of anomaly detection for large scale networks is a key way to promptly detect the abnormal traffic flows. In this paper, priori triggered BP neural network algorithm(PBP) is analyzed for the purpose of dealing with the problems caused by typical algorithms that are not able to adapt and learn; detect with high precision; provide high level of correctness. PBP uses K-Means and PCA to trigger self-adapting and learning ability, and also, it uses historical neuron parameter to initialize the neural network, so that it use the trained network to detect the abnormal traffic flows. According to experiments, PBP can obtain a higher level of correctness of detection than priori algorithm, and it can adapt itself according to different network environments.

The Analysis of Faults Detection Software Based on Improved Neural Network Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2044-2047
Author(s):  
Miao Yan ◽  
Zhi Bao Liu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Improved Method ◽  
Software Faults ◽  
Component Software ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Detection Software

The large-scale software is consisted of the components which are quite different. The detection accuracy of the traditional faults detection methods for the large-scale component software is not satisfactory. This paper proposes a large-scale software faults detection methods based on improved neural network combining the features of the large-scale software by computing the stable probability and building the neural network faults detection models. The proposed method can analyze the serial faults of the large-scale software to determine the positions of the faults. The experiment and simulation results show that the improved method for large-scale software fault detection can greatly improve the accuracy.

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.

scholarly journals Application of Neural Network Algorithm in Optimal Control of Ethylene Distillation Tower

2021 ◽  
pp. 19-29
Author(s):  
Ruyang Mo ◽  
Huihui Wang
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Simulation Software ◽  
Network Algorithms ◽  
System Description ◽  
Software Interface ◽  
Operation Speed ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Rectification Process

For some nonlinear dynamic systems with uncertainties or disturbances, neural networks can perform intelligent cognition and simulation on them, achieve a good system description, and further realize intelligent control. Aiming at the ethylene rectification process, in order to avoid the time delay of complex rectification process modeling and large-scale process simulation software interface program, and to improve the simulation operation speed, the optimization model combined with the learning function of the neural network is used for the simulation calculation of the rectification process. It can meet the time and accuracy requirements of online optimization. This article outlines several commonly used neural network algorithms and their related applications in ethylene distillation, aiming to provide reference for the development and innovation of industry technology.

scholarly journals The Design of Neural Network Based Accuracy Prediction for Software Testing Black-Box A Method with the Equivalence Partitions Technique

2020 ◽  
Vol 8 (9) ◽  
pp. 01-04
Author(s):  
Zulkifli et al.
Keyword(s):  
Neural Network ◽  
Software Testing ◽  
Black Box ◽  
Real Output ◽  
The Real ◽  
Network Algorithm ◽  
The Neural Network ◽  
Accuracy Level ◽  
Neural Network Algorithm ◽  
Box Method

The absence of a neural network algorithm model to predict the level of accuracy in terms of black-box software testing, equivalence partitions technique is a problem in this research. In this case, the algorithm used for predicting software errors by researchers is the neural network algorithm and testing the software uses the black-box method with the equivalence partitions technique. The neural network algorithm is an artificial neural system, or neural network are the physical cellular system that can acquire, store and use the knowledge gained from experience for activation using bipolar sigmoid output values which range between -1 to 1. Software testing black-box methods is a testing approach where the data comes from defined functional requirements regardless of the final program structure, and the technique used is equivalence partitions. The design prediction accuracy of this research is by determining the college application to be the software to be tested, then tested using the black-box method with the equivalence partitions technique (this test chosen because it will find software errors in several categories, including functions error or missing, interface errors, errors in data structures or external database access, performance errors, initialization errors and terminations), from the black-box test the dataset obtained. This dataset measures the accuracy of the real output and prediction output. The last step is calculating the error, RSME from the real output and prediction output. The results of this research show that the neural network algorithm was being to measure the accuracy level of software testing applied to determine the prediction of the accuracy level of black-box software testing with the equivalence partitions technique, and the average accuracy results are above 80%.

scholarly journals Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by an FPGA

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2090 ◽  
Author(s):  
Tanghao Jia ◽  
Tianle Guo ◽  
Xuming Wang ◽  
Dan Zhao ◽  
Chang Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Response Speed ◽  
Fast Response ◽  
Mixed Gas ◽  
Cross Sensitivity ◽  
Network Algorithm ◽  
The Neural Network ◽  
Gas Identification ◽  
Neural Network Algorithm

It is a daunting challenge to measure the concentration of each component in natural gas, because different components in mixed gas have cross-sensitivity for a single sensor. We have developed a mixed gas identification device based on a neural network algorithm, which can be used for the online detection of natural gas. The neural network technology is used to eliminate the cross-sensitivity of mixed gases to each sensor, in order to accurately recognize the concentrations of methane, ethane and propane, respectively. The neural network algorithm is implemented by a Field-Programmable Gate Array (FPGA) in the device, which has the advantages of small size and fast response. FPGAs take advantage of parallel computing and greatly speed up the computational process of neural networks. Within the range of 0–100% of methane, the test error for methane and heavy alkanes such as ethane and propane is less than 0.5%, and the response speed is several seconds.

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