**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**3414

# Search results for: Algorithm LSQR.

##### 3414 An Iterative Method for the Symmetric Arrowhead Solution of Matrix Equation

**Authors:**
Minghui Wang,
Luping Xu,
Juntao Zhang

**Abstract:**

**Keywords:**
Symmetric arrowhead matrix,
iterative method,
like-minimum norm,
minimum norm,
Algorithm LSQR.

##### 3413 An Iterative Method for the Least-squares Symmetric Solution of AXB+CYD=F and its Application

**Authors:**
Minghui Wang

**Abstract:**

Based on the classical algorithm LSQR for solving (unconstrained) LS problem, an iterative method is proposed for the least-squares like-minimum-norm symmetric solution of AXB+CYD=E. As the application of this algorithm, an iterative method for the least-squares like-minimum-norm biymmetric solution of AXB=E is also obtained. Numerical results are reported that show the efficiency of the proposed methods.

**Keywords:**
Matrix equation,
bisymmetric matrix,
least squares problem,
like-minimum norm,
iterative algorithm.

##### 3412 A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem

**Authors:**
Gaohuizi Guo,
Ning Zhang

**Abstract:**

**Keywords:**
Firefly algorithm,
hybrid algorithm,
multi-objective optimization,
Sine Cosine algorithm.

##### 3411 Approximating Fixed Points by a Two-Step Iterative Algorithm

**Authors:**
Safeer Hussain Khan

**Abstract:**

In this paper, we introduce a two-step iterative algorithm to prove a strong convergence result for approximating common fixed points of three contractive-like operators. Our algorithm basically generalizes an existing algorithm..Our iterative algorithm also contains two famous iterative algorithms: Mann iterative algorithm and Ishikawa iterative algorithm. Thus our result generalizes the corresponding results proved for the above three iterative algorithms to a class of more general operators. At the end, we remark that nothing prevents us to extend our result to the case of the iterative algorithm with error terms.

**Keywords:**
Contractive-like operator,
iterative algorithm,
fixed point,
strong convergence.

##### 3410 Some Improvements on Kumlander-s Maximum Weight Clique Extraction Algorithm

**Authors:**
Satoshi Shimizu,
Kazuaki Yamaguchi,
Toshiki Saitoh,
Sumio Masuda

**Abstract:**

Some fast exact algorithms for the maximum weight clique problem have been proposed. Östergard’s algorithm is one of them. Kumlander says his algorithm is faster than it. But we confirmed that the straightforwardly implemented Kumlander’s algorithm is slower than O¨ sterga˚rd’s algorithm. We propose some improvements on Kumlander’s algorithm.

**Keywords:**
Maximum weight clique,
exact algorithm,
branch-andbound,
NP-hard.

##### 3409 Application of Adaptive Genetic Algorithm in Function Optimization

**Authors:**
Panpan Xu,
Shulin Sui

**Abstract:**

The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.

**Keywords:**
Genetic algorithm,
Adaptive genetic algorithm,
Function optimization.

##### 3408 Optimal External Merge Sorting Algorithm with Smart Block Merging

**Authors:**
Mir Hadi Seyedafsari,
Iraj Hasanzadeh

**Abstract:**

**Keywords:**
External sorting algorithm,
internal sortingalgorithm,
fast sorting,
robust algorithm.

##### 3407 Analog Circuit Design using Genetic Algorithm: Modified

**Authors:**
Amod P. Vaze

**Abstract:**

Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to analog circuit design automation. These researches show a better performance due to the nature of Genetic Algorithm. In this paper a modified Genetic Algorithm is applied for analog circuit design automation. The modifications are made to the topology of the circuit. These modifications will lead to a more computationally efficient algorithm.

**Keywords:**
Genetic algorithm,
analog circuits,
design.

##### 3406 Application of Hybrid Genetic Algorithm Based on Simulated Annealing in Function Optimization

**Authors:**
Panpan Xu,
Shulin Sui,
Zongjie Du

**Abstract:**

**Keywords:**
Genetic algorithm,
Simulated annealing,
Hybrid
genetic algorithm,
Function optimization.

##### 3405 Convergence Analysis of an Alternative Gradient Algorithm for Non-Negative Matrix Factorization

**Authors:**
Chenxue Yang,
Mao Ye,
Zijian Liu,
Tao Li,
Jiao Bao

**Abstract:**

Non-negative matrix factorization (NMF) is a useful computational method to find basis information of multivariate nonnegative data. A popular approach to solve the NMF problem is the multiplicative update (MU) algorithm. But, it has some defects. So the columnwisely alternating gradient (cAG) algorithm was proposed. In this paper, we analyze convergence of the cAG algorithm and show advantages over the MU algorithm. The stability of the equilibrium point is used to prove the convergence of the cAG algorithm. A classic model is used to obtain the equilibrium point and the invariant sets are constructed to guarantee the integrity of the stability. Finally, the convergence conditions of the cAG algorithm are obtained, which help reducing the evaluation time and is confirmed in the experiments. By using the same method, the MU algorithm has zero divisor and is convergent at zero has been verified. In addition, the convergence conditions of the MU algorithm at zero are similar to that of the cAG algorithm at non-zero. However, it is meaningless to discuss the convergence at zero, which is not always the result that we want for NMF. Thus, we theoretically illustrate the advantages of the cAG algorithm.

**Keywords:**
Non-negative matrix factorizations,
convergence,
cAG
algorithm,
equilibrium point,
stability.

##### 3404 Genetic Mining: Using Genetic Algorithm for Topic based on Concept Distribution

**Authors:**
S. M. Khalessizadeh,
R. Zaefarian,
S.H. Nasseri,
E. Ardil

**Abstract:**

**Keywords:**
Genetic Algorithm,
Text Mining,
Term Weighting,
Concept Extraction,
Concept Distribution.

##### 3403 Application of ESA in the CAVE Mode Authentication

**Authors:**
Keonwoo Kim,
Dowon Hong,
Kyoil Chung

**Abstract:**

**Keywords:**
ESA,
CAVE,
CDMA,
authentication,
mobilecommunication.

##### 3402 A New Algorithm to Stereo Correspondence Using Rank Transform and Morphology Based On Genetic Algorithm

**Authors:**
Razagh Hafezi,
Ahmad Keshavarz,
Vida Moshfegh

**Abstract:**

**Keywords:**
genetic algorithm,
morphology,
rank transform,
stereo correspondence

##### 3401 A Genetic Based Algorithm to Generate Random Simple Polygons Using a New Polygon Merge Algorithm

**Authors:**
Ali Nourollah,
Mohsen Movahedinejad

**Abstract:**

In this paper a new algorithm to generate random simple polygons from a given set of points in a two dimensional plane is designed. The proposed algorithm uses a genetic algorithm to generate polygons with few vertices. A new merge algorithm is presented which converts any two polygons into a simple polygon. This algorithm at first changes two polygons into a polygonal chain and then the polygonal chain is converted into a simple polygon. The process of converting a polygonal chain into a simple polygon is based on the removal of intersecting edges. The experiments results show that the proposed algorithm has the ability to generate a great number of different simple polygons and has better performance in comparison to celebrated algorithms such as space partitioning and steady growth.

**Keywords:**
Divide and conquer,
genetic algorithm,
merge
polygons,
Random simple polygon generation.

##### 3400 A Constrained Clustering Algorithm for the Classification of Industrial Ores

**Authors:**
Luciano Nieddu,
Giuseppe Manfredi

**Abstract:**

**Keywords:**
K-means,
Industrial ores classification,
Invariant Features,
Supervised Classification.

##### 3399 An Innovative Fuzzy Decision Making Based Genetic Algorithm

**Authors:**
M. A. Sharbafi,
M. Shakiba Herfeh,
Caro Lucas,
A. Mohammadi Nejad

**Abstract:**

**Keywords:**
Genetic Algorithm,
Fuzzy Decision Making.

##### 3398 FILMS based ANC System – Evaluation and Practical Implementation

**Authors:**
Branislav Vuksanović,
Dragana Nikolić

**Abstract:**

**Keywords:**
Active noise control,
adaptive filters,
inverse filters,
LMS algorithm,
FILMS algorithm.

##### 3397 Simulation of Tracking Time Delay Algorithm using Mathcad Package

**Authors:**
Mahmud Hesain ALdwaik,
Omar Hsiain Eldwaik

**Abstract:**

This paper deals with tracking and estimating time delay between two signals. The simulation of this algorithm accomplished by using Mathcad package is carried out. The algorithm we will present adaptively controls and tracking the delay, so as to minimize the mean square of this error. Thus the algorithm in this case has task not only of seeking the minimum point of error but also of tracking the change of position, leading to a significant improving of performance. The flowchart of the algorithm is presented as well as several tests of different cases are carried out.

**Keywords:**
Tracking time delay,
Algorithm simulation,
Mathcad,
MSE

##### 3396 A New Algorithm for Cluster Initialization

**Authors:**
Moth'd Belal. Al-Daoud

**Abstract:**

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the k-means algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximum variance. The algorithm has been applied to different data sets and good results are obtained.

**Keywords:**
clustering,
k-means,
data mining.

##### 3395 Improving the Performance of Back-Propagation Training Algorithm by Using ANN

**Authors:**
Vishnu Pratap Singh Kirar

**Abstract:**

Artificial Neural Network (ANN) can be trained using back propagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a twoterm algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

**Keywords:**
Neural Network,
Backpropagation,
Local Minima,
Fast Convergence Rate.

##### 3394 A New Evolutionary Algorithm for Cluster Analysis

**Authors:**
B.Bahmani Firouzi,
T. Niknam,
M. Nayeripour

**Abstract:**

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.

**Keywords:**
Data clustering,
Hybrid evolutionary optimization algorithm,
K-means algorithm,
Simulated Annealing (SA),
Particle Swarm Optimization (PSO).

##### 3393 Scheduling Multiple Workflow Using De-De Dodging Algorithm and PBD Algorithm in Cloud: Detailed Study

**Authors:**
B. Arun Kumar,
T. Ravichandran

**Abstract:**

**Keywords:**
Workflow Scheduling,
cloud workflow,
TCHC
algorithm,
De-De Dodging Algorithm,
Priority Based Decisive
Algorithm (PBD),
Makespan.

##### 3392 Adaptive Fuzzy Control on EDF Scheduling

**Authors:**
Xiangbin Zhu

**Abstract:**

EDF (Early Deadline First) algorithm is a very important scheduling algorithm for real- time systems . The EDF algorithm assigns priorities to each job according to their absolute deadlines and has good performance when the real-time system is not overloaded. When the real-time system is overloaded, many misdeadlines will be produced. But these misdeadlines are not uniformly distributed, which usually focus on some tasks. In this paper, we present an adaptive fuzzy control scheduling based on EDF algorithm. The improved algorithm can have a rectangular distribution of misdeadline ratios among all real-time tasks when the system is overloaded. To evaluate the effectiveness of the improved algorithm, we have done extensive simulation studies. The simulation results show that the new algorithm is superior to the old algorithm.

**Keywords:**
Fuzzy control,
real-time systems,
EDF,
misdeadline ratio.

##### 3391 A Comparative Study of GTC and PSP Algorithms for Mining Sequential Patterns Embedded in Database with Time Constraints

**Authors:**
Safa Adi

**Abstract:**

**Keywords:**
Database,
GTC algorithm,
PSP algorithm,
sequential
patterns,
time constraints.

##### 3390 An IM-COH Algorithm Neural Network Optimization with Cuckoo Search Algorithm for Time Series Samples

**Authors:**
Wullapa Wongsinlatam

**Abstract:**

**Keywords:**
Artificial neural networks,
back propagation
algorithm,
time series,
local minima problem,
metaheuristic
optimization.

##### 3389 Solving the Quadratic Assignment Problems by a Genetic Algorithm with a New Replacement Strategy

**Authors:**
Yongzhong Wu,
Ping Ji

**Abstract:**

**Keywords:**
Quadratic assignment problem,
Genetic algorithm,
Replacement strategy,
QAPLIB.

##### 3388 A Optimal Subclass Detection Method for Credit Scoring

**Authors:**
Luciano Nieddu,
Giuseppe Manfredi,
Salvatore D'Acunto,
Katia La Regina

**Abstract:**

In this paper a non-parametric statistical pattern recognition algorithm for the problem of credit scoring will be presented. The proposed algorithm is based on a clustering k- means algorithm and allows for the determination of subclasses of homogenous elements in the data. The algorithm will be tested on two benchmark datasets and its performance compared with other well known pattern recognition algorithm for credit scoring.

**Keywords:**
Constrained clustering,
Credit scoring,
Statistical pattern recognition,
Supervised classification.

##### 3387 A Practical Distributed String Matching Algorithm Architecture and Implementation

**Authors:**
Bi Kun,
Gu Nai-jie,
Tu Kun,
Liu Xiao-hu,
Liu Gang

**Abstract:**

**Keywords:**
Boyer-Moore algorithm,
distributed algorithm,
parallel string matching,
string matching.

##### 3386 Using the Polynomial Approximation Algorithm in the Algorithm 2 for Manipulator's Control in an Unknown Environment

**Authors:**
Pavel K. Lopatin,
Artyom S. Yegorov

**Abstract:**

The Algorithm 2 for a n-link manipulator movement amidst arbitrary unknown static obstacles for a case when a sensor system supplies information about local neighborhoods of different points in the configuration space is presented. The Algorithm 2 guarantees the reaching of a target position in a finite number of steps. The Algorithm 2 is reduced to a finite number of calls of a subroutine for planning a trajectory in the presence of known forbidden states. The polynomial approximation algorithm which is used as the subroutine is presented. The results of the Algorithm2 implementation are given.

**Keywords:**
Manipulator,
trajectory planning,
unknown obstacles.

##### 3385 A Transform Domain Function Controlled VSSLMS Algorithm for Sparse System Identification

**Authors:**
Cemil Turan,
Mohammad Shukri Salman

**Abstract:**

**Keywords:**
Adaptive filtering,
sparse system identification,
VSSLMS algorithm,
TD-LMS algorithm.