In this paper, we propose an algorithm for classification based on extreme learning machine, named TS-ELM (Two feature Spaces Extreme learning Machine). It is a hybridization of two distinct feature mapping of SLFNs (Single hidden layer feedforward neural network). Both are realized based on ELM theories. The first feature space resulted from a random kernel distributions of a randomly connected input samples, that inspired initially from conventional random connected neural network and it is also included in ELM theories. The second feature space that gives the training or testing samples is the result of a random probability distribution of fully connected samples taken from the first feature space, and adjusted with their own weight and threshold parameters. This algorithm was applied on three datasets of multiclass and binary classification, and the results shows that it has strong classification capabilities and universal approximation as well. The mechanisms of the algorithm and universal approximation are discussed in this paper.

In this paper, we propose an algorithm for classification based on extreme learning machine, named TS-ELM (Two feature Spaces Extreme learning Machine). It is a hybridization of two distinct feature mapping of SLFNs (Single hidden layer feedforward neural network). Both are realized based on ELM theories. The first feature space resulted from a random kernel distributions of a randomly connected input samples, that inspired initially from conventional random connected neural network and it is also included in ELM theories. The second feature space that gives the training or testing samples is the result of a random probability distribution of fully connected samples taken from the first feature space, and adjusted with their own weight and threshold parameters. This algorithm was applied on three datasets of multiclass and binary classification, and the results shows that it has strong classification capabilities and universal approximation as well. The mechanisms of the algorithm and universal approximation are discussed in this paper.

In this paper, we propose an algorithm for classification based on extreme learning machine, named TS-ELM (Two feature Spaces Extreme learning Machine). It is a hybridization of two distinct feature mapping of SLFNs (Single hidden layer feedforward neural network). Both are realized based on ELM theories. The first feature space resulted from a random kernel distributions of a randomly connected input samples, that inspired initially from conventional random connected neural network and it is also included in ELM theories. The second feature space that gives the training or testing samples is the result of a random probability distribution of fully connected samples taken from the first feature space, and adjusted with their own weight and threshold parameters. This algorithm was applied on three datasets of multiclass and binary classification, and the results shows that it has strong classification capabilities and universal approximation as well. The mechanisms of the algorithm and universal approximation are discussed in this paper.

The ENICAB company in Biskra uses the cold drawing process on several types of wires rod of different materials and grades. Our study was carried out on an Al-Mg-Si (AA6101) aluminum alloy wire rod, the most used by the ENICAB company in the manufacture of electrical energy transmission cables. The purpose of this work is to understand the evolution of the deformation texture and the stored energy in the grains during cold drawing of wire, as well as the combined influence of deformation and annealing at 400 °C during different holding time on recrystallization kinetics and evolution of local and global crystallographic texture. Characterization methods used in this work is: Optical Microscopy (OM), Scanning Electron Microscopy (SEM), Back Scatter Electron Diffraction (EBSD), X-ray diffraction, Neutron diffraction, Vickers microhardness and Chemical analysis by EDS.

The ENICAB company in Biskra uses the cold drawing process on several types of wires rod of different materials and grades. Our study was carried out on an Al-Mg-Si (AA6101) aluminum alloy wire rod, the most used by the ENICAB company in the manufacture of electrical energy transmission cables. The purpose of this work is to understand the evolution of the deformation texture and the stored energy in the grains during cold drawing of wire, as well as the combined influence of deformation and annealing at 400 °C during different holding time on recrystallization kinetics and evolution of local and global crystallographic texture. Characterization methods used in this work is: Optical Microscopy (OM), Scanning Electron Microscopy (SEM), Back Scatter Electron Diffraction (EBSD), X-ray diffraction, Neutron diffraction, Vickers microhardness and Chemical analysis by EDS.

The ENICAB company in Biskra uses the cold drawing process on several types of wires rod of different materials and grades. Our study was carried out on an Al-Mg-Si (AA6101) aluminum alloy wire rod, the most used by the ENICAB company in the manufacture of electrical energy transmission cables. The purpose of this work is to understand the evolution of the deformation texture and the stored energy in the grains during cold drawing of wire, as well as the combined influence of deformation and annealing at 400 °C during different holding time on recrystallization kinetics and evolution of local and global crystallographic texture. Characterization methods used in this work is: Optical Microscopy (OM), Scanning Electron Microscopy (SEM), Back Scatter Electron Diffraction (EBSD), X-ray diffraction, Neutron diffraction, Vickers microhardness and Chemical analysis by EDS.

The ENICAB company in Biskra uses the cold drawing process on several types of wires rod of different materials and grades. Our study was carried out on an Al-Mg-Si (AA6101) aluminum alloy wire rod, the most used by the ENICAB company in the manufacture of electrical energy transmission cables. The purpose of this work is to understand the evolution of the deformation texture and the stored energy in the grains during cold drawing of wire, as well as the combined influence of deformation and annealing at 400 °C during different holding time on recrystallization kinetics and evolution of local and global crystallographic texture. Characterization methods used in this work is: Optical Microscopy (OM), Scanning Electron Microscopy (SEM), Back Scatter Electron Diffraction (EBSD), X-ray diffraction, Neutron diffraction, Vickers microhardness and Chemical analysis by EDS.

The ENICAB company in Biskra uses the cold drawing process on several types of wires rod of different materials and grades. Our study was carried out on an Al-Mg-Si (AA6101) aluminum alloy wire rod, the most used by the ENICAB company in the manufacture of electrical energy transmission cables. The purpose of this work is to understand the evolution of the deformation texture and the stored energy in the grains during cold drawing of wire, as well as the combined influence of deformation and annealing at 400 °C during different holding time on recrystallization kinetics and evolution of local and global crystallographic texture. Characterization methods used in this work is: Optical Microscopy (OM), Scanning Electron Microscopy (SEM), Back Scatter Electron Diffraction (EBSD), X-ray diffraction, Neutron diffraction, Vickers microhardness and Chemical analysis by EDS.

The ENICAB company in Biskra uses the cold drawing process on several types of wires rod of different materials and grades. Our study was carried out on an Al-Mg-Si (AA6101) aluminum alloy wire rod, the most used by the ENICAB company in the manufacture of electrical energy transmission cables. The purpose of this work is to understand the evolution of the deformation texture and the stored energy in the grains during cold drawing of wire, as well as the combined influence of deformation and annealing at 400 °C during different holding time on recrystallization kinetics and evolution of local and global crystallographic texture. Characterization methods used in this work is: Optical Microscopy (OM), Scanning Electron Microscopy (SEM), Back Scatter Electron Diffraction (EBSD), X-ray diffraction, Neutron diffraction, Vickers microhardness and Chemical analysis by EDS.