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 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.

Study on Neural Networks Usage to Analyse Correlation between Spectrum of Vibration Acceleration Signal from Pin of Ball Mill and its Filling Level

2015 ◽  
Vol 770 ◽  
pp. 540-546 ◽  
Author(s):  
Yuri Eremenko ◽  
Dmitry Poleshchenko ◽  
Anton Glushchenko
Keyword(s):  
Neural Network ◽  
Ball Mill ◽  
Signal Amplitude ◽  
Signal Spectrum ◽  
Laboratory Model ◽  
Training Set ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
The Neural Network ◽  
Trained Neural Network

The question about modern intelligent information processing methods usage for a ball mill filling level evaluation is considered. Vibration acceleration signal has been measured on a mill laboratory model for that purpose. It is made with accelerometer attached to a mill pin. The conclusion is made that mill filling level can not be measured with the help of such signal amplitude only. So this signal spectrum processed by a neural network is used. A training set for the neural network is formed with the help of spectral analysis methods. Trained neural network is able to find the correlation between mill pin vibration acceleration signal and mill filling level. Test set is formed from the data which is not included into the training set. This set is used in order to evaluate the network ability to evaluate the mill filling degree. The neural network guarantees no more than 7% error in the evaluation of mill filling level.

scholarly journals PREDICTION OF NONLINEAR SYSTEM IN OPTICS USING GENETIC PROGRAMMING

2007 ◽  
Vol 18 (03) ◽  
pp. 369-374 ◽  
Author(s):  
AMR RADI
Keyword(s):  
Experimental Data ◽  
Refractive Index ◽  
Nonlinear System ◽  
Genetic Programming ◽  
Prediction Model ◽  
Output Data ◽  
Fiber Core ◽  
Input And Output ◽  
Gp Model

It is difficult to predict the dynamics of systems which are nonlinear and whose characteristic is unknown. In order to build a model of the system from input and output data without any knowledge about the system, we try automatically to build prediction model by Genetic Programming (GP). GP has been used to discover the function that describes nonlinear system to study the effect of wavelength and temperature on the refractive index of the fiber core. The predicted distribution from the GP based model is compared with the experimental data. The discovered function of the GP model has proved to be an excellent match to the experimental data.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

scholarly journals Power Quality Disturbance Identification and Optimization Based on Machine Learning

2021 ◽  
Author(s):  
Fei Long ◽  
Fen Liu ◽  
Xiangli Peng ◽  
Zheng Yu ◽  
Huan Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Classification Method ◽  
Neural Network Training ◽  
Training Set ◽  
Network Training ◽  
The Neural Network ◽  
Recognition Ability ◽  
Power Quality Disturbance ◽  
Depth Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

scholarly journals Neural Network Effectiveness Evaluation While the Intersection of Images in the Receptor Field

2021 ◽  
Vol 17 ◽  
pp. 172-176
Author(s):  
Siranush Sargsyan ◽  
Anna Hovakimyan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Probabilistic Approach ◽  
Effectiveness Evaluation ◽  
Training Set ◽  
Receptor Field ◽  
Network Effectiveness ◽  
The Neural Network

The study and application of neural networks is one of the main areas in the field of artificial intelligence. The effectiveness of the neural network depends significantly on both its architecture and the structure of the training set. This paper proposes a probabilistic approach to evaluate the effectiveness of the neural network if the images intersect in the receptor field. A theorem and its corollaries are proved, which are consistent with the results obtained by a different path for a perceptron-type neural network.

A UNIVERSAL NEURAL NETWORK REPRESENTATION FOR HADRON–HADRON INTERACTIONS AT HIGH ENERGY

2000 ◽  
Vol 11 (03) ◽  
pp. 619-628 ◽  
Author(s):  
KHALED. A. EL-METWALLY ◽  
TAREK I. HAWEEL ◽  
MAHMOUD Y. EL-BAKRY
Keyword(s):  
Neural Network ◽  
Multiplicity Distribution ◽  
High Energy ◽  
Training Set ◽  
Charged Multiplicity ◽  
Proton Proton ◽  
Hadron Interaction ◽  
Network Representation ◽  
The Neural Network

An efficient neural network (NN) has been designed to simulate the hadron–hadron interaction at high energy. Two cases have been considered simultaneously, the proton–proton (p–p) and the pion–proton (π-p) interactions. The neural network has been trained to produce the charged multiplicity distribution for both cases based on samples from the overlapping functions. The trained NN shows a good performance in matching the trained distributions. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The robustness of the designed NN in the presence of uncertainties, in the overlapping functions has been demonstrated.

Critical Knock Diagnosis for Gasoline Engines Based on Neural Network with Wavelet Transform and Fuzzy Clustering

2012 ◽  
Vol 455-456 ◽  
pp. 1084-1089
Author(s):  
Jian Guo Yang ◽  
Yan Yan Wang ◽  
Bo Lin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Gasoline Engine ◽  
Engine Performance ◽  
Discrete Wavelet ◽  
New Approach ◽  
Gasoline Engines ◽  
Input And Output ◽  
The Neural Network ◽  
The Common

. It is difficult to detect critical knock for a gasoline engine by the common method of knock diagnosis. In this paper, a new approach is presented to detect critical knock for gasoline engines. Based on this approach knock diagnosis consists of four steps. Firstly, discrete wavelet transform (DWT) is chosen as a pre-processor for a neural network to extract knock characteristic signals; Secondly, four characteristic factors are selected and calculated from knock characteristic signals; Thirdly, degree of memberships of the characteristic factors are calculated as the input and output of the neural network; and finally a RBF(Radial Basis Function) neural network is chosen, trained and applied to detect critical knock. Knock experiments were performed on a gasoline engine, and the application of the presented approach was studied. The results show that the presented method is practicable and can be applied to control the ignition of a gasoline engine working under critical knock which is admitted as an improved state of engine performance.

Application of Wavelet Neural Network in Cortisol Solubility

2011 ◽  
Vol 396-398 ◽  
pp. 711-715
Author(s):  
Jian Xin Chen ◽  
Xu Na Shi ◽  
Shu Chun Pang ◽  
Mei Jing Zhang ◽  
Sheng Yu Li
Keyword(s):  
Neural Network ◽  
Wavelet Transforms ◽  
Learning Algorithm ◽  
Wavelet Neural Network ◽  
Training Data ◽  
Robust Learning ◽  
Input And Output ◽  
The Neural Network ◽  
Adaptively Adjusting ◽  
Flow Of Information

Wavelet neural network(WNN) was applied to predicate the cortisol solubility. The model consists of a multilayer feedforward hierarchical structure, and the flow of information is directed from the input to the output layer by using wavelet transforms to achieve faster convergence. By adaptively adjusting the number of training data involved during training, an adaptive robust learning algorithm is derived for improvement of the efficiency of the network. The neural network was trained and simulated cortisol solubility with different input and output parameters. Simulation results confirmed that this approach gave more accurate predictions solubility.

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