Showing papers by "Mahdi Aliyari Shoorehdeli published in 2006"

A novel training algorithm in ANFIS structure

Abstract: This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with gradient decent (GD) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from genetic algorithm (GA) method. The simulation results show that in comparison with current GD training, the novel training can have a better adaptation to complex plants. Also, the results show this new hybrid approach optimizes ANFIS parameters faster and better parameters than gradient base method.

Feature Subset Selection for Face Detection Using Genetic Algorithms and Particle Swarm Optimization

Abstract: This paper has introduced a new method for feature subset selection to which less attention has been given. The most of the past works have emphasized feature extraction, classification and using classical methods for these works. The main goal in feature extraction is presented data in lower dimension. One of the popular methods in feature extraction is principle component analysis (PCA). This method and similar methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper we introduced particle swarm optimization (PSO) as a simple, general and powerful framework for selecting good subsets of features, leading to improved detection rates. We used PCA for feature extraction and support vector machines (SVMs) for classification. The goal is to search the PCA space using PSO to select a subset of eigenvectors encoding important information about the target concept of interest. An other object in this paper is to increase speed of convergence by using PSO to find the best feature. We have tested the frame work in mind on challenging application like face detection. Our results illustrate the significant improvement in this case.

Velocity Control of an Electro Hydraulic Servosystem

Abstract: This paper addresses new classic approaches for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. In our new approaches, we used the classical method based-on Lyapanov function. It is demonstrated that this new technique have good control ability performance. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of a nonlinear mathematical model of the system. The controllers which were introduced have a big range for the control of the system