scholarly journals VULNERABILITY INDEX ASSESSMENT USING NEURAL NETWORKS (VIANN): A CASE STUDY OF NICOSIA, CYPRUS

2017 ◽  
Vol IV-4/W4 ◽  
pp. 189-195
Author(s):  
H. Dindar ◽  
K. Dimililer ◽  
Ö. C. Özdağ ◽  
C. Atalar ◽  
M. Akgün ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Cost Effective ◽  
Vulnerability Index ◽  
Back Propagation Neural Network ◽  
Zone Velocity ◽  
Output Neurons ◽  
Microtremor Measurement ◽  
Artificial Neural Network Ann

Many scholars have used microtremor applications to evaluate the vulnerability index. In order to reach fast and reliable results, microtremor measurement is preferred as it is a cost-effective method. In this paper, the vulnerability index will be reviewed by utilization of microtremor measurement results in Nicosia city. 100 measurement stations have been used to collect microtremor data and the data were analysed by using Nakamura’s method. The value of vulnerability index (Kg) has been evaluated by using the fundamental frequency and amplification factor. The results obtained by the artificial neural network (ANN) will be compared with microtremor measurements. Vulnerability Index Assessment using Neural Networks (VIANN) is a backpropagation neural network, which uses the original input microtremor Horizontal Vertical Spectrum Ratio (HVSR) spectrum set. A 3-layer back propagation neural network which contains 4096 input, 28 hidden and 3 output neurons are used in this suggested system. The output neurons are classified according to acceleration sensitivity zone, velocity zones, or displacement zones. The sites are classified by their vulnerability index values using binary coding: [1 0 0] for the acceleration sensitive zone, [0 1 0] for the velocity sensitive zone, and [0 0 1] for the displacement sensitive zone.

Meteorological Tidal Predictions in the Mekong Estuary Using an Evolved ANN Time Series

2019 ◽  
Vol 53 (6) ◽  
pp. 27-34
Author(s):  
Tim Chen ◽  
C.Y.J. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Variation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Systems ◽  
Modeling And Forecasting ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

AbstractThe reproduction of meteorological waves utilizing physically based hydrodynamic models is very difficult in light of the fact that it requires enormous amounts of information, for example, hydrological and water-driven time arrangement limits, stream geometry, and balance coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modeling and forecasting the maximum and time variation of meteorological tsunamis in the Mekong Estuary in Vietnam. The parameters, including both the nearby climatic and breeze field factors, for finding the most extreme meteorological waves are first examined, depending on the preparation of the evolved neural systems. The time series for meteorological tsunamis are used for training and testing the models, and data for three cyclones are used for model prediction. This study finds that the proposed advanced ANN time series model is easy to utilize with display and prediction tools for simulating the time variation of meteorological tsunamis.

scholarly journals Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks

2010 ◽  
Vol 61 (4) ◽  
pp. 235-240 ◽  
Author(s):  
Perumal Chandrasekar ◽  
Vijayarajan Kamaraj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Voltage Sag ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.

scholarly journals Automatical Knowledge Representation of Logical Relations by Dynamical Neural Network

2017 ◽  
Vol 26 (4) ◽  
pp. 625-639 ◽  
Author(s):  
Gang Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Representation ◽  
Network Structure ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ann Model ◽  
Functional Approximation ◽  
Dynamical Neural Network

AbstractCurrently, most artificial neural networks (ANNs) represent relations, such as back-propagation neural network, in the manner of functional approximation. This kind of ANN is good at representing the numeric relations or ratios between things. However, for representing logical relations, these ANNs have disadvantages because their representation is in the form of ratio. Therefore, to represent logical relations directly, we propose a novel ANN model called probabilistic logical dynamical neural network (PLDNN). Inhibitory links are introduced to connect exciting links rather than neurons so as to inhibit the connected exciting links conditionally to make them represent logical relations correctly. The probabilities are assigned to the weights of links to indicate the belief degree in logical relations under uncertain situations. Moreover, the network structure of PLDNN is less limited in topology than traditional ANNs, and it is dynamically built completely according to the data to make it adaptive. PLDNN uses both the weights of links and the interconnection structure to memorize more information. The model could be applied to represent logical relations as the complement to numeric ANNs.

Prediction of Human Responses to Dairy Odor Using an Electronic Nose and Neural Networks

2018 ◽  
Vol 61 (2) ◽  
pp. 399-409 ◽  
Author(s):  
Fangle Chang ◽  
Paul Heinemann
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Electronic Nose ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Sources Of Information ◽  
Independent Validation ◽  
Quantitative Measurements ◽  
Resilient Back Propagation ◽  
Human Assessment

Abstract. Odor emitted from dairy operations may cause negative reactions by farm neighbors. Identification and evaluation of such malodors is vital for better understanding of human response and methods for mitigating effects of odors. The human nose is a valuable tool for odor assessment, but using human panels can be costly and time-consuming, and human evaluation of odor is subjective. Sensing devices, such as an electronic nose, have been widely used to measure volatile emissions from different materials. The challenge, though, is connecting human assessment of odors with the quantitative measurements from instruments. In this work, a prediction system was designed and developed to use instruments to predict human assessment of odors from common dairy operations. The model targets are the human responses to odor samples evaluated using a general pleasantness scale ranging from -11 (extremely unpleasant) to +11 (extremely pleasant). The model inputs were the electronic nose measurements. Three different neural networks, a Levenberg-Marquardt back-propagation neural network (LMBNN), a scaled conjugate gradient back-propagation neural network (CGBNN), and a resilient back-propagation neural network (RPBNN), were applied to connect these two sources of information (human assessments and instrument measurements). The results showed that the LMBNN model can predict human assessments with accuracy as high as 78% within a 10% range and as high as 63% within a 5% range of the targets in independent validation. In addition, the LMBNN model performed with the best stability in both training and independent validation. Keywords: Animal production, Hedonic tone, Olfactometric models.

scholarly journals Different ANN Models for Short Term Electricity Price Forecasting

2019 ◽  
Vol 8 (3) ◽  
pp. 6706-6712
Keyword(s):  
Neural Network ◽  
Market Price ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Price Forecasting ◽  
Market Prices ◽  
Electricity Price Forecasting ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Pool Market

In a deregulated electiricity market, price forecasting is gaining demand with application of Artificial Neural Network (ANN). The paper deals with price forecasting with different ANN models.like Back Propagation Neural Network( BPNN), Radial Bias Function Neural Network (RBFNN) and Genectic Algorithm based Neural Network (GANN). A contextual investigation is made with the downloaded data of the day-ahead pool market prices of the California Pool Market using the above four different ANN models and the results are compared.

scholarly journals DEVELOPMENT OF BIOMETRIC METHODS AND INFORMATION SECURITY TOOLS

2021 ◽  
Vol 2 (336) ◽  
pp. 121-124
Author(s):  
T. Zh. Mazakov ◽  
D. N. Narynbekovna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Future Perspective ◽  
Data Set

Now a day’s security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.

Offline Signature Recognition using Back Propagation Neural Network

2016 ◽  
Vol 4 (3) ◽  
pp. 678
Author(s):  
Asyrofa Rahmi ◽  
Vivi Nur Wijayaningrum ◽  
Wayan Firdaus Mahmudy ◽  
Andi Maulidinnawati A. K. Parewe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Learning Rate ◽  
Signature Recognition ◽  
Artificial Neural Network Ann ◽  
Number Of Iterations

The signature recognition is a difficult process as it requires several phases. A failure in a phase will significantly reduce the recognition accuracy. Artificial Neural Network (ANN) believed to be used to assist in the recognition or classification of the signature. In this study, the ANN algorithm used is Back Propagation. A mechanism to adaptively adjust the learning rate is developed to improve the system accuracy. The purpose of this study is to conduct the recognition of a number of signatures so that can be known whether the recognition which is done by using the Back Propagation is appropriate or not. The testing results performed by using learning rate of 0.64, the number of iterations is 100, and produces an accuracy value of 63%.

Short-Term Traffic Flow Forecasting of Road Based on Spline Weight Function Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 695-698
Author(s):  
Dai Yuan Zhang ◽  
Jian Hui Zhan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Weight Function ◽  
Traffic Flow ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Forecasting Method

Traditional short-term traffic flow forecasting of road usually based on back propagation neural network, which has a low prediction accuracy and convergence speed. This paper introduces a spline weight function neural networks which has a feature that the weight function can well reflect sample information after training, thus propose a short-term traffic flow forecasting method base on the spline weight function neural network, specify the network learning algorithm, and make a comparative tests bases on the actual data. The result proves that in short-term traffic flow forecasting, the spline weight function neural network is more effective than traditional methods.

scholarly journals Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 551 ◽  
Author(s):  
Baraka Mathew Nkurlu ◽  
Chuanbo Shen ◽  
Solomon Asante-Okyere ◽  
Alvin K. Mulashani ◽  
Jacqueline Chungu ◽  
...  
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Group Method ◽  
Well Testing ◽  
Well Log ◽  
Data Handling ◽  
Permeability Model ◽  
Log Data ◽  
Artificial Neural Network Ann

Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

Pressure Measurement and Pattern Recognition by Using Neural Networks

2005 ◽  
Author(s):  
Parham Piroozan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Pressure Sensor ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Moiré Fringes ◽  
Flexible Wall ◽  
Four Levels ◽  
Fringe Patterns

This paper describes an intelligent control system that uses electro-optics and neural networks to control the flow of air over a flexible wall. In this investigation a pressure sensor which was part of the wall of the wind tunnel and an optical apparatus were used to produce moire´ fringes. A back propagation neural network was used to analyze the fringe patterns and to classify the pressures into four levels. A second neural network was used to recognize the pressure patterns and to provide the input to a control system that was capable of modifying the shape of the flexible wall in order to preserve the stability of the flow. The flexible wall was part of the wall of the wind tunnel and was installed in the upstream of the flow. It was made of silicone rubber and had an area of 76 mm by 76 mm. There were 15 rows of actuators installed under the flexible wall which were used to change the shape of the wall. In the downstream of the flow was an optical pressure sensor which had the same dimensions as the flexible wall and consisted of a 15 × 15 array of small diaphragms. These diaphragms responded to the pressure fluctuations in the boundary layer flow and were the source of the signals for the optical system. A CCD camera viewed the pressure sensor through an optical apparatus which produced moire´ fringes. A back propagation neural network analyzed the fringe patterns and classified the pressures into four levels. The classified pressures which was a 15 × 15 array of numbers ranging from 1 to 4 was the input to a second back propagation neural network which was used to recognize the pressure patterns. The output from the back propagation neural network used for pattern recognition provided the input to a control system that changed the shape of the flexible wall. This paper presents the experimental results as well as the computer simulations which were created for this project. This includes the complete process of creating the slope fringes, classifying the pressures into four levels, recognizing the wall pressure patterns and generating the output signals to the actuator for changing the shape of the flexible wall.

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