scholarly journals Neural Networks for Fingerprint Recognition

1993 ◽  
Vol 5 (3) ◽  
pp. 402-418 ◽  
Author(s):  
Pierre Baldi ◽  
Yves Chauvin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Base ◽  
Error Rate ◽  
Fingerprint Recognition ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed.

scholarly journals Implementation of the Neural Network Algorithm in Advanced Databases

2019 ◽  
Vol 16 (2) ◽  
Author(s):  
Nedeljko Šikanjić ◽  
Zoran Ž. Avramović ◽  
Esad F. Jakupović
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

The progress of humanity is closely related to the progress of technology. The highlight of the development of technology will occur when the machines are able to do what they learn and know; think and make decisions on their own without human help. In this paper we will try to analyze how neural networks work and how they will further develop in terms of application in advanced databases. We will also explore how one of the major IT companies is developing and continuing to use neural networks.

Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

2011 ◽  
Vol 90-93 ◽  
pp. 2173-2177
Author(s):  
Chen Cai ◽  
Tao Huang ◽  
Xun Li ◽  
Yun Zhen Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Water Inflow ◽  
Water Volume ◽  
Network Algorithm ◽  
Forecast Precision ◽  
Neural Network Algorithm ◽  
Computation Analysis

The submarine tunnel water-inflow question has many kinds of factor synthesis influences, has highly the complexity and the misalignment, This article used the BP neural network algorithm to establish the submarine tunnel welling up water volume forecast model and to carry on the computation analysis, The result indicated that this model restraining performance is good, the forecast precision is high and simple feasible. This method has provided a new mentality for the submarine tunnel welling up water volume's forecast.

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.

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 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.

Sign in / Sign up

Export Citation Format

Share Document