A Comparative Approximate Economic Behavior Analysis of Support Vector Machines and Neural Networks Models

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data

This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values.  Seismic surveying was performed next on these models. F...

A Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels

The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...

Toward a Thorough Approach to Predicting Klinkenberg Permeability in a Tight Gas Reservoir: A Comparative Study

Klinkenberg permeability is an important parameter in tight gas reservoirs. There are conventional methods for determining it, but these methods depend on core permeability. Cores are few in number, but well logs are usually accessible for all wells and provide continuous information. In this regard, regression methods have been used to achieve reliable relations between log readings and Klinke...

Identification and Adaptive Position and Speed Control of Permanent Magnet DC Motor with Dead Zone Characteristics Based on Support Vector Machines

In this paper a new type of neural networks known as Least Squares Support Vector Machines which gained a huge fame during the recent years for identification of nonlinear systems has been used to identify DC motor with nonlinear dead zone characteristics. The identified system after linearization in each time span, in an online manner provide the model data for Model Predictive Controller of p...