scholarly journals Influence of Data Scaling and Normalization on Overall Neural Network Performances in Photoacoustics

2021 ◽  
Author(s):  
Miroslava Ivko Jordovic Pavlovic ◽  
Katarina Djordjevic ◽  
Zarko Cojbasic ◽  
Slobodanka Galovic ◽  
Marica Popovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Photoacoustic Signal ◽  
Output Data ◽  
Network Training ◽  
Input And Output ◽  
The Neural Network ◽  
Data Scaling ◽  
Logarithmic Scaling ◽  
Network Prediction

Abstract In this paper, the influence of the input and output data scaling and normalization on the neural network overall performances is investigated aimed at inverse problem-solving in photoacoustics of semiconductors. The logarithmic scaling of the photoacoustic signal amplitudes as input data and numerical scaling of the sample thermal parameters as output data are presented as useful tools trying to reach maximal network precision. Max and min-max normalizations to the input data are presented to change their numerical values in the dataset to common scales, without distorting differences. It was demonstrated in theory that the largest network prediction error of all targeted parameters is obtained by a network with non-scaled output data. Also, it was found out that the best network prediction was achieved with min-max normalization of the input data and network predicted output data scale within the range of [110]. Network training and prediction performances analyzed with experimental input data show that the benefits and improvements of input and output scaling and normalization are not guaranteed but are strongly dependent on a specific problem to be solved.

REPRESENTATION FOR DISPERSION FORMULA OF OPTICAL FIBER USING HYBRID TECHNIQUE

2008 ◽  
Vol 19 (02) ◽  
pp. 205-213 ◽  
Author(s):  
AMR RADI
Keyword(s):  
Neural Network ◽  
Refractive Index ◽  
Optical Fiber ◽  
Hybrid Technique ◽  
Training Set ◽  
Fiber System ◽  
Output Data ◽  
Input And Output ◽  
Dispersion Formula ◽  
The Neural Network

Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) which represents dispersion formula of optical fiber. An efficient NN has been designed by GA to simulate the dynamics of the optical fiber system which is nonlinear. Without any knowledge about the system, we have used the input and output data to build a prediction model by NN. The neural network has been trained to produce a function that describes nonlinear system which studies the dependence of the refractive index of the fiber core on the wavelength and temperature. The trained NN model shows a good performance in matching the trained distributions. The NN is then used to predict refractive index that is not presented in the training set. The predicted refractive index had been matched to the experimental data effectively.

scholarly journals Expert judgement-based tuning of the system reliability neural network

2013 ◽  
Vol 21 (1) ◽  
pp. 28-34
Author(s):  
A. Brandowski ◽  
Hoang Nguyen ◽  
Wojciech Frąckowiak
Keyword(s):  
Neural Network ◽  
Correlation Analysis ◽  
System Reliability ◽  
Expert Judgement ◽  
Data Correlation ◽  
Ahp Method ◽  
Output Data ◽  
Input And Output ◽  
The Neural Network ◽  
Technical Systems

ABSTRACT The neural network tuning procedure applied to reliability analyses of anthrop technical systems, based on judgements of experts - experienced operating practicians. Numerical and linguistic elicitation of the judgements, analyses of the network input and output data correlation and of the AHP method processing deviation are presented. Example of data elicitation and correlation analysis of a reliability arrangement of the seagoing ship propulsion system are included to the article.

scholarly journals System Identification of Neural Signal Transmission Based on Backpropagation Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiangyu Li ◽  
Chunhua Yuan ◽  
Bonan Shan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Signal Transmission ◽  
Neural Model ◽  
Neural Firing ◽  
Output Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Network Training ◽  
Input And Output

The identification method of backpropagation (BP) neural network is adopted to approximate the mapping relation between input and output of neurons based on neural firing trajectory in this paper. In advance, the input and output data of neural model is used for BP neural network learning, so that the identified BP neural network can present the transfer characteristics of the model, which makes the network precisely predict the firing trajectory of the neural model. In addition, the method is applied to identify electrophysiological experimental data of real neurons, so that the output of the identified BP neural network can not only accurately fit the neural firing trajectories of neurons participating in the network training but also predict the firing trajectories and spike moments of neurons which are not involved in the training process with high accuracy.

Using One-Dimensional Linear Interpolation Method to Check Over-Fitting in Neural Network with Multi-Dimensional Inputs

2014 ◽  
Vol 496-500 ◽  
pp. 2228-2232 ◽  
Author(s):  
Bao Lien Hung ◽  
Hsin An Hung
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
One Dimensional ◽  
Input And Output ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Linear Interpolation Method ◽  
Fitting In

Neural networks are widely used to learn and predict the correlation between input and output. However, in the process of learning, the excessive reduction of errors may influence the validity of prediction, this phenomenon is called over-fitting. In order to address this problem, this study sequenced the input data into one-dimensional data for the neural network structure of multi-dimensional inputs, and used visual graphics to observe whether there is over-fitting. This method is called one-dimensional linear interpolation method. The result of example validation proved that the proposed method can provide specific graphical information effectively, and determine whether there is over-fitting.

scholarly journals OPTIMIZATION OF THE INPUT DATA VECTOR TO IMPROVE THE NEURAL NETWORK TRAINING FOR OPC

2021 ◽  
Author(s):  
Georgiy Teplov ◽  
Almira Galeeva ◽  
Aleksey Kuzovkov
Keyword(s):  
Neural Network ◽  
Data Structure ◽  
Input Data ◽  
Input Vector ◽  
Neural Network Training ◽  
Network Training ◽  
The Neural Network ◽  
Data Vector ◽  
The Impact

This work explored the impact of input data structure to improve the neural network training. The impact of two variants of the input data vector on the training accuracy of the network was studied. The first version of the input vector included the intensity of the exposure radiation map. The second version of the input vector included the intensity of the exposure radiation map and IC topology.

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

2021 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Network Training ◽  
The Neural Network ◽  
Continental Hydrology ◽  
Terrestrial Water ◽  
Combine Machine ◽  
Groundwater Basins

<p>Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.</p><p>https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258</p>

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

scholarly journals The BP Network Classification Leafminer-Infected Leaves Based on the Fractal Dimension

2011 ◽  
Vol 1 ◽  
pp. 163-167
Author(s):  
Da Ke Wu ◽  
Chun Yan Xie
Keyword(s):  
Neural Network ◽  
Fractal Dimension ◽  
Correlation Coefficient ◽  
Bp Neural Network ◽  
Input Data ◽  
Bp Network ◽  
Neural Network Prediction ◽  
Infected Tomato ◽  
Network Prediction ◽  
Hidden Nodes

Leafminer is one of pest of many vegetables, and the damage may cover so much of the leaf that the plant is unable to function, and yields are noticeably decreased. In order to get the information of the pest in the vegetable before the damage was not serious, this research used a BP neural network to classify the leafminer-infected tomato leaves, and the fractal dimension of the leaves was the input data of the BP neural network. Prediction results showed that when the number of FD was 21 and the hidden nodes of BP neural network were 21, the detection performance of the model was good and the correlation coefficient (r) was 0.836. Thus, it is concluded that the FD is an available technique for the detection of disease level of leafminer on tomato leaves.

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

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