Seyed Hamid Zahiri Articles List

Open Access Article

1 - Multi-Objective Particle Swarm Classifier
Seyed-Hamid Zahiri

20.1001.1.16823745.1385.4.8.6.2

A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The propos More

A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed classifier can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not have any over-fitting and over-learning problems. Other swarm intelligence based classifiers do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem. The experimental results show that the proposed multi-objective classifier can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices. Manuscript profile

Open Access Article

2 - Novel Automatic Clustering Technique Based on the Artificial Immune Algorithm
Seyed-Hamid Zahiri

20.1001.1.16823745.1387.6.14.12.4

In this paper a novel technique for automatic data clustering based on the artificial immune algorithm is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been a More

In this paper a novel technique for automatic data clustering based on the artificial immune algorithm is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been added to the immune algorithm to provide, also, a soft computing approach for data clustering. This idea leads to proper number of clusters and effective and powerful clustering process without any additional try and error efforts. Also the manual setting of the number of clusters is available in the proposed algorithm (like other unsupervised clustering approaches) after removing the fuzzy controller from the proposed clustering system. The method has been tested on the different kinds of the complex artificial data sets and well known benchmarks. The experimental results show that the performance of the proposed technique is much better than the k-means clustering algorithm (as a conventional one), specially for huge data sets with large feature vector dimensions. Furthermore, it is found that the performance of the proposed approach is comparable, sometimes better than the genetic algorithm based clustering technique (as an evolutionary clustering algorithm). Manuscript profile

Open Access Article

3 - Training of MLP Neural Network for Data Classification by GSA Method
M. Dehbashian Seyed-Hamid Zahiri

20.1001.1.16823745.1389.8.4.5.1

Nowadays, several techniques have presented for data classification. One of these techniques is neural network that has attracted many interests. In this classifier, selection a suitable learning method is very important for training of the network. Error back propagati More

Nowadays, several techniques have presented for data classification. One of these techniques is neural network that has attracted many interests. In this classifier, selection a suitable learning method is very important for training of the network. Error back propagation is the most usual training method of neural networks that late convergence and stopping in local optimum points are its weakness. New approach in neural networks training is the usage of heuristic algorithms. This paper suggests a new learning method namely gravitational search algorithm (GSA) in training of neural network for data classification. GSA method is the latest and the most novel version of swarm intelligence optimization methods. This algorithm is inspired fby the law of Newtonian gravity and mass concept in nature. In this paper, a MLP neural network is trained for classification of five benchmark data set by GSA method. Also, the proposed method efficiency in training and testing of neural network compared with those of two training methods error back propagation and particle swarm optimization. Final results showed the GSA method extraordinary performance for data correct classification in most of cases. Also, in these experiments the GSA method produced stable results in all of cases. In addition, the run time of GSA method is shorter than that of the PSO. Manuscript profile

Open Access Article

4 - Designing Optimal Fuzzy Classifier Using Particle Swarm Optimization
Seyed-Hamid Zahiri

20.1001.1.16823745.1391.10.4.5.5

An important issue in designing a fuzzy classifier is setting its structural and mathematical fuzzy parameters (e.g., number of rules, antecedents, consequents, types and locations of membership functions). In fact, the variations of these parameters establish a wide More

An important issue in designing a fuzzy classifier is setting its structural and mathematical fuzzy parameters (e.g., number of rules, antecedents, consequents, types and locations of membership functions). In fact, the variations of these parameters establish a wide range high dimensional search space, which makes heuristic methods some suitable candidates to solve this problem (designing optimal fuzzy parameters). In this paper, a method is described for this purpose. In presented technique, all fuzzy parameters of a fuzzy classifier, are interpreted in structure of particles and PSO algorithm is employed to find the optimal one. Extensive experimental results on well-known benchmarks and practical pattern recognition problem (automatic target recognition) demonstrate the effectiveness of the proposed method. Manuscript profile

Open Access Article

5 - Unsupervised Image Clustering Using Central Force Optimization Algorithm Unsupervised Image Clustering Using Central Force Optimization Algorithm
M. H. Mozafari Maref Seyed-Hamid Zahiri

20.1001.1.16823745.1392.11.2.6.9

Central Force Optimization (CFO) is a new member of heuristic algorithms which has been recently proposed and added to swarm intelligence algorithms. In this paper, an effective unsupervised image clustering technique is proposed, using CFO and called CFO-clustering. In More

Central Force Optimization (CFO) is a new member of heuristic algorithms which has been recently proposed and added to swarm intelligence algorithms. In this paper, an effective unsupervised image clustering technique is proposed, using CFO and called CFO-clustering. In the presented method, each probe includes the information of center of the clusters, and fitness function contains both inter-distance and intra-distance of the samples. Extensive experimental results show that the proposed CFO-clustering outperforms other similar clustering algorithms which were designed based on the evolutionary techniques. Manuscript profile

Open Access Article

6 - A Novel Source/Drain side Double Recessed Gate 4H-SiC MESFET with n-Buried Layer in the Channel
S. M. Razavi Seyed-Hamid Zahiri

20.1001.1.16823745.1396.15.2.8.9

A new structure named as source/drain sides-double recessed gate with N-buried layer in the channel (SDS-DRG) silicon carbide (SiC) based metal semiconductor field effect transistor (MESFET) is presented in this study. Important parameters such as short channel effect, More

A new structure named as source/drain sides-double recessed gate with N-buried layer in the channel (SDS-DRG) silicon carbide (SiC) based metal semiconductor field effect transistor (MESFET) is presented in this study. Important parameters such as short channel effect, maximum DC trans-conductance, drain current and breakdown voltage of the proposed structure are simulated and compared with those of the source side-double recessed gate (SS-DRG) and drain side-double recessed gate (DS-DRG) 4H-SiC MESFETs. Our simulation results reveal that reducing the channel thickness under the gate at the SDS-DRG structure improves the maximum DC trans-conductance and reduces the short channel effects compared to SS-DRG and DS-DRG structures. Reducing the channel thickness under the gate at the drain side of the SDS-DRG structure is used to enhance the breakdown voltage in comparison with the SS-DRG structure. Also, N-buried layer with larger doping concentration in the SDS-DRG structure improves the saturated drain current compared to SS-DRG and DS-DRG structures.‏ Manuscript profile

Open Access Article

7 - Investigation of the Novel Attributes at AlGaN/GaN HEMT with a P-Layer in the Barrier at Source and Drain Side
S. M. Razavi Seyed-Hamid Zahiri S. E. Hosseini

20.1001.1.16823745.1396.15.3.8.1

In this work, novel gallium-nitride (GaN) high electron mobility transistor (HEMT) with a p-layer in the barrier at source and drain sides (SD-PL) is reported. Important parameters such as gate-source and gate-drain capacitances, maximum DC trans-conductance (gm), cut o More

In this work, novel gallium-nitride (GaN) high electron mobility transistor (HEMT) with a p-layer in the barrier at source and drain sides (SD-PL) is reported. Important parameters such as gate-source and gate-drain capacitances, maximum DC trans-conductance (gm), cut off frequency (fT), maximum lateral electric field, breakdown voltage, DC output conductance (go) and saturated drain current of the proposed structure are studied in details using two-dimensional and two-carrier device simulations. The simulation results of the proposed structure are compared with those of the source side p-layer in the barrier (S-PL), drain side p-layer in the barrier (D-PL) and conventional structures. According to the extracted results, the proposed structure improves the gate-source capacitance, maximum gm, cut off frequency and go compared to the D-PL structure. Also this new structure reduces the peak electric field at the gate corner near the drain and consequently increases the breakdown voltage significantly in comparison with the conventional structure. Increasing p-layer length (LP) and thickness (TP) in the SD-PL and S-PL structures, improves the breakdown voltage, gate-source capacitance, gate-drain capacitance and go.‏ Manuscript profile

Open Access Article

8 - Proposing an Intelligent Method for Design and Optimization of Double tail Comparator
Sadegh Mohammadi-Esfahrood Seyed-Hamid Zahiri

20.1001.1.16823745.1399.18.3.13.7

The performance of an Analog/Digital (A/D) converter, various aspects like general architecture of the converter, architecture of the building blocks or design of the blocks can be improved. The comparator block is a fundamental block in data converters. Due to contradi More

The performance of an Analog/Digital (A/D) converter, various aspects like general architecture of the converter, architecture of the building blocks or design of the blocks can be improved. The comparator block is a fundamental block in data converters. Due to contradicting design purposes, circuit constraints and necessities, design of comparators and obtaining best circuit performance are complicated and challenging. Such challenges in circuit design necessitate presenting approaches which not only satisfy all the objectives but also, they are cost effective in terms of time and cost. One of the approaches which has recently attracted attentions is the heuristic algorithms based intelligent Methods. Inclined Planes system Optimization algorithm (IPO) is a novel heuristic algorithm inspired by dynamic movement of the objects on frictionless inclined planes. But despite its remarkable ability for exploration and exploitation of the search space, its standard model has complex relationships with many structural parameters that often confuse the user in choosing the effective values for them. In this paper, IPO algorithm is simplified to present a heuristic algorithm (called SIPO) and its efficiency in optimization of 10 standard benchmarks has been evaluated. Then, a multi-objective version of the proposed algorithm (called MOSIPO) for design and optimization of double tail comparator is presented and its efficiency in optimization of double tail comparator has been evaluated and compared with popular multi-objective intelligent methods. The results clearly demonstrate the improved performance and superiority of SIPO and MOSIPO compared to the other methods. Manuscript profile

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