An efficient non linear algorithm predictive model of a robust optimal portfolio

2019 ◽  
Vol 13 (17) ◽  
pp. 805-813
Author(s):  
I. Irakoze ◽  
F. Nahayo
Keyword(s):  
Predictive Model ◽  
Optimal Portfolio ◽  
Linear Algorithm ◽  
Non Linear

scholarly journals Flicker spreading in a transmission network

2020 ◽  
Vol 69 (1) ◽  
pp. 18-22
Author(s):  
Tonko Garma ◽  
Denisa Galzina
Keyword(s):  
Linear Regression ◽  
Predictive Model ◽  
Cost Effective ◽  
Transmission Network ◽  
Transmission Grid ◽  
Key Concepts ◽  
Regression Approach ◽  
Non Linear

This paper reports the flicker spreading in the transmission network. Chapter 1 presents introduction containing brief background and key concepts, followed by description of the corresponding instrumentation in Chapter 2.  Key contribution of the paper is elaborated in Chapters 3 and 4.  Chapter 3 reports measurements of the flicker magnitude along the 400 kV, 220 kV and 110 kV transmission grid for various distances from flicker origin on 400 kV grid, and Chapter 4 gives cost-effective predictive model, enabling estimation of the flicker magnitude for arbitrary selected origin-to-spot distance base on non-linear regression approach. Paper is extension of the work presented at Smagrimet 2019 conference.

Development of predictive models for effective process parameter selection for single and overlapping laser clad bead geometry

2018 ◽  
Vol 24 (1) ◽  
pp. 214-228 ◽  
Author(s):  
Kush Aggarwal ◽  
R.J. Urbanic ◽  
Syed Mohammad Saqib
Keyword(s):  
Data Collection ◽  
Predictive Model ◽  
Laser Cladding ◽  
Process Planning ◽  
Process Parameters ◽  
Predictive Models ◽  
Goodness Of Fit ◽  
Bead Geometry ◽  
Content Type ◽  
Non Linear

Purpose The purpose of this work is to explore predictive model approaches for selecting laser cladding process settings for a desired bead geometry/overlap strategy. Complementing the modelling challenges is the development of a framework and methodologies to minimize data collection while maximizing the goodness of fit for the predictive models. This is essential for developing a foundation for metallic additive manufacturing process planning solutions. Design/methodology/approach Using the coaxial powder flow laser cladding method, 420 steel cladding powder is deposited on low carbon structural steel plates. A design of experiments (DOE) approach is taken using the response surface methodology (RSM) to establish the experimental configuration. The five process parameters such as laser power, travel speed, etc. are varied to explore their impact on the bead geometry. A total of three replicate experiments are performed and the collected data are assessed using a variety of methods to determine the process trends and the best modelling approaches. Findings There exist unpredictable, non-linear relationships between the process parameters and the bead geometry. The best fit for a predictive model is achieved with the artificial neural network (ANN) approach. Using the RSM, the experimental set is reduced by an order of magnitude; however, a model with R2 = 0.96 is generated with ANN. The predictive model goodness of fit for a single bead is similar to that for the overlapping bead geometry using ANN. Originality/value Developing a bead shape to process parameters model is challenging due to the non-linear coupling between the process parameters and the bead geometry and the number of parameters to be considered. The experimental design and modelling approaches presented in this work illustrate how designed experiments can minimize the data collection and produce a robust predictive model. The output of this work will provide a solid foundation for process planning operations.

Prediction on Combat Effectiveness of Amphibious Assault Vehicles Based on Supporting-Vector Regression

2014 ◽  
Vol 1044-1045 ◽  
pp. 829-832
Author(s):  
Mian Hao Qiu ◽  
Hua Cong ◽  
Hua Peng Jia
Keyword(s):  
Predictive Model ◽  
Feature Space ◽  
High Dimensional ◽  
Non Linear ◽  
Combat Effectiveness ◽  
Decision Functions

In this paper, a predictive model has been established for the combat effectiveness of amphibious assault vehicles by the method of supporting-vector regression. It has a good fitting result after trained with the samples. Then this model is used to predict the combat effectiveness of the sample under evaluation. The SVR approach can convert the practical questions into the high-dimensional feature space through non-linear transfer, and construct the linear decision functions in the space to realize the original non-linear decision functions. This approach has its significance for the prediction of combat effectiveness of AAVs.

Sign in / Sign up

Export Citation Format

Share Document