Non-linear Predictive Models for Speech Processing

2005 ◽  
pp. 779-784
Author(s):  
M. Chetouani ◽  
Amir Hussain ◽  
M. Faundez-Zanuy ◽  
B. Gas
Keyword(s):  
Speech Processing ◽  
Predictive Models ◽  
Non Linear

Non-Linear Speech Processing (NOLISP 2013)

2015 ◽  
Vol 30 (1) ◽  
pp. 1-2
Author(s):  
Thomas Drugman
Keyword(s):  
Speech Processing ◽  
Non Linear

Special issue on non-linear and non-conventional speech processing

2009 ◽  
Vol 51 (9) ◽  
pp. 713
Author(s):  
Mohamed Chetouani ◽  
Marcos Faundez-Zanuy ◽  
Amir Hussain ◽  
Bruno Gas ◽  
Jean-Luc Zarader ◽  
...  
Keyword(s):  
Speech Processing ◽  
Special Issue ◽  
Non Linear

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.

Advances in Non-Linear Modeling for Speech Processing

2012 ◽  
Author(s):  
Raghunath S. Holambe ◽  
Mangesh S. Deshpande
Keyword(s):  
Speech Processing ◽  
Linear Modeling ◽  
Non Linear

scholarly journals Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

2020 ◽  
Vol 7 ◽  
Author(s):  
David Medina-Ortiz ◽  
Sebastián Contreras ◽  
Cristofer Quiroz ◽  
Álvaro Olivera-Nappa
Keyword(s):  
Supervised Learning ◽  
Predictive Models ◽  
Non Linear

scholarly journals Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

2010 ◽  
Vol 2 (3) ◽  
pp. 133-134 ◽  
Author(s):  
Jordi Solé-Casals ◽  
Vladimir Zaiats ◽  
Enric Monte-Moreno
Keyword(s):  
Speech Processing ◽  
Non Linear ◽  
Alternative Techniques

Flexible non-linear predictive models for large-scale wind turbine diagnostics

Wind Energy ◽  
2016 ◽  
Vol 20 (5) ◽  
pp. 753-764 ◽  
Author(s):  
Martin Bach-Andersen ◽  
Bo Rømer-Odgaard ◽  
Ole Winther
Keyword(s):  
Wind Turbine ◽  
Predictive Models ◽  
Large Scale ◽  
Non Linear
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