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Application of various methods for 2D inverse modeling of residual gravity anomalies

Abedi M., Afshar A., Ardestani V.E., Narouzi G., Lucas C.

Acta Geophysica

2010

Vol. 58, no. 2

317-336

This paper presents a neural network approach to determine 2D inverse modeling of a buried structure from gravity anomaly profile. The results of the applied neural network method are compared with the results of two other methods, least-squares minimization and the simple method. Sphere, horizontal cylinder and vertical cylinder and their gravity effects are considered as the synthetic models and the synthetic data, respectively. The synthetic data are also corrupted with noise to evaluate the capability of the methods. Then the Dehloran bitumen map in Iran is chosen as a real data application. Anomaly value of the cross-section, which is taken from the gravity anomaly map of Dehloran bitumen, is very close to those obtained from these methods.

Emotional learning based intelligent speed and position control applied to neurofuzzy model of switched reluctance motor

Rouhani H., Sadeghzadeh A., Lucas C., Araabi B. N.

Control and Cybernetics

2007

Vol. 36, no 1

75-95

In this paper, rotor speed and position of a Switched Reluctance Motor (SRM) are controlled using an intelligent control algorithm. The controller is working based on a PID signal while its gain is permanently tuned by means of an Emotional Learning Algorithm to achieve a better control performance. Here, nonlinear characteristic of SRM is identified using an efficient training algorithm (LoLiMoT) for Locally Linear Neurofuzzy Model as an unspecified nonlinear plant model. Then, the Brain Emotional Learning Based Intelligent Controller (BELBIC) is applied to the obtained model. While the intelligent controller works based on a computational model of a limbic system in the mammalian brain, its contribution is to improve the performance of a classic controller like PID without much more control effort. The results demonstrate excellent improvements of control action in different working situations.