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.

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.

GENETIC PROGRAMMING FOR HADRONIC INTERACTIONS AT HIGH ENERGIES

2007 ◽  
Vol 18 (03) ◽  
pp. 329-334 ◽  
Author(s):  
M. Y. EL-BAKRY ◽  
A. RADI
Keyword(s):  
Experimental Data ◽  
Genetic Programming ◽  
Rapidity Distribution ◽  
Proton Proton ◽  
Hadronic Interactions ◽  
High Energies ◽  
Gp Model

Genetic programming (GP) has been used to discover a function that describes pseudo-rapidity distribution of created pions from proton–proton (p-p) interactions at high and ultra-high energies. The predicted distributions from the GP-based model are compared with the experimental data. The discovered function of GP model has proven matching better for experimental data.

scholarly journals A Profit Distribution Model of Reverse Logistics Based on Fuzzy DEA Efficiency—Modified Shapley Value

2021 ◽  
Vol 13 (13) ◽  
pp. 7354
Author(s):  
Jiekun Song ◽  
Xiaoping Ma ◽  
Rui Chen
Keyword(s):  
Shapley Value ◽  
Reverse Logistics ◽  
Service Providers ◽  
Third Party ◽  
Output Data ◽  
Profit Distribution ◽  
Logistics Service ◽  
Input And Output ◽  
Value Model ◽  
Fuzzy Dea

Reverse logistics is an important way to realize sustainable production and consumption. With the emergence of professional third-party reverse logistics service providers, the outsourcing model has become the main mode of reverse logistics. Whether the distribution of cooperative profit among multiple participants is fair or not determines the quality of the implementation of the outsourcing mode. The traditional Shapley value model is often used to distribute cooperative profit. Since its distribution basis is the marginal profit contribution of each member enterprise to different alliances, it is necessary to estimate the profit of each alliance. However, it is difficult to ensure the accuracy of this estimation, which makes the distribution lack of objectivity. Once the actual profit share deviates from the expectation of member enterprise, the sustainability of the reverse logistics alliance will be affected. This study considers the marginal efficiency contribution of each member enterprise to the alliance and applies it to replace the marginal profit contribution. As the input and output data of reverse logistics cannot be accurately separated from those of the whole enterprise, they are often uncertain. In this paper, we assume that each member enterprise’s input and output data are fuzzy numbers and construct an efficiency measurement model based on fuzzy DEA. Then, we define the characteristic function of alliance and propose a modified Shapley value model to fairly distribute cooperative profit. Finally, an example comprising of two manufacturing enterprises, one sales enterprise, and one third-party reverse logistics service provider is put forward to verify the model’s feasibility and effectiveness. This paper provides a reference for the profit distribution of the reverse logistics.

Investigations of the Optical Properties of LaNi5 and LaNi4.5Sn0.5

2013 ◽  
Vol 321-324 ◽  
pp. 495-498 ◽  
Author(s):  
Dong Chen ◽  
Chao Xu
Keyword(s):  
Experimental Data ◽  
Refractive Index ◽  
Optical Properties ◽  
Density Functional ◽  
Low Frequency ◽  
Potential Method ◽  
Frequency Region ◽  
Functional Theory ◽  
The Density Functional Theory ◽  
Pseudo Potential

The reflectivity, loss function, refractive index, extinction coefficient and dielectric function of the LaNi5and LaNi4.5Sn0.5intermetallic compounds are investigated through the plane-wave pseudo-potential method based on the density functional theory. The effects of Sn impurity are discussed and some interesting features are found in the low frequency region. Some important optical properties such as static dielectric constant and static refractive index are obtained. The equation [n (0)]2=ε1(0)is satisfied according to our calculation, which indicates that our results are correct and reasonable. Nevertheless, the calculated results need to be testified in the future due to the lack of experimental data.

A new genetic programming model for predicting settlement of shallow foundations

2007 ◽  
Vol 44 (12) ◽  
pp. 1462-1473 ◽  
Author(s):  
Mohammad Rezania ◽  
Akbar A. Javadi
Keyword(s):  
Genetic Programming ◽  
Programming Model ◽  
Standard Penetration Test ◽  
Shallow Foundations ◽  
Penetration Test ◽  
Case Histories ◽  
Contributing Factors ◽  
Artificial Neural Network Ann ◽  
Gp Model ◽  
Very High

In this paper, a new genetic programming (GP) approach for predicting settlement of shallow foundations is presented. The GP model is developed and verified using a large database of standard penetration test (SPT) based case histories that involve measured settlements of shallow foundations. The results of the developed GP model are compared with those of a number of commonly used traditional methods and artificial neural network (ANN) based models. It is shown that the GP model is able to learn, with a very high accuracy, the complex relationship between foundation settlement and its contributing factors, and render this knowledge in the form of a function. The attained function can be used to generalize the learning and apply it to predict settlement of foundations for new cases not used in the development of the model. The advantages of the proposed GP model over the conventional and ANN based models are highlighted.

scholarly journals Data derived NARMAX Dst model

2011 ◽  
Vol 29 (6) ◽  
pp. 965-971 ◽  
Author(s):  
R. J. Boynton ◽  
M. A. Balikhin ◽  
S. A. Billings ◽  
A. S. Sharma ◽  
O. A. Amariutei
Keyword(s):  
Mathematical Model ◽  
Solar Wind ◽  
System Identification ◽  
Nonlinear Systems ◽  
Wide Class ◽  
Geomagnetic Storms ◽  
Output Data ◽  
Dst Index ◽  
Geomagnetic Indices ◽  
Input And Output

Abstract. The NARMAX OLS-ERR methodology is applied to identify a mathematical model for the dynamics of the Dst index. The NARMAX OLS-ERR algorithm, which is widely used in the field of system identification, is able to identify a mathematical model for a wide class of nonlinear systems using input and output data. Solar wind-magnetosphere coupling functions, derived from analytical or data based methods, are employed as the inputs to such models and the outputs are geomagnetic indices. The newly deduced coupling function, p1/2V4/3BTsin6(θ/2), has been implemented as an input to model the Dst dynamics. It was shown that the identified model has a very good forecasting ability, especially with the geomagnetic storms.

An LQ Controller With a Prescribed Pole Region—A Data-Based Design Approach

1997 ◽  
Vol 119 (2) ◽  
pp. 271-277 ◽  
Author(s):  
Jenq-Tzong H. Chan
Keyword(s):  
Closed Loop ◽  
Explicit Knowledge ◽  
Controller Design ◽  
Design Approach ◽  
Output Data ◽  
Plant Model ◽  
Modified Method ◽  
Input And Output ◽  
Z Domain

In this paper, we present a modified method of data-based LQ controller design which is distinct in two major aspects: (1) one may prescribe the z-domain region within which the closed-loop poles of the LQ design are to lie, and (2) controller design is completed using only plant input and output data, and does not require explicit knowledge of a parameterized plant model.

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