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2015
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Optimization of Jk Flip Flop Layout with Minimal Average Power of Consumption Based on Acor, Fuzzy-acor, GA, and Fuzzy-GA
Optimization of Jk Flip Flop Layout with Minimal Average Power of Consumption Based on Acor, Fuzzy-acor, GA, and Fuzzy-GA
en
en
The object of heuristic algorithms is to produce an optimum solution for solving a problem. When the number of variables in the problem is high the Heuristic Algorithms are used. In this article the goal is to find an optimum layout for JK Flip Flop for minimizing the average power. There are twenty MOSFETs with different channel widths. They make a twenty dimensional search space which are independent decision variables. Motivated by the convergence of Ant Colony Optimization in real domain (ACOR) and Genetic Algorithm (GA) and the link of MATLAB with HSPICE Software the optimized layout of JK Flip Flop is obtained. Based on ACOR, Fuzzy-ACOR, GA, Fuzzy-GA algorithms the best resulting JK Flip Flop layout in CMOS Technology with supply voltage of 5v has the average power consumption of 1.6 nW with Fuzzy-ACOR.
1
15
Farshid
Keivanian
Ali
Yekta
Nasser
Mehrshad
Optimum layout
JK Flip Flop
ACOR
Fuzzy-ACOR
GA
Fuzzy-GA.
Article.1.pdf
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[1]
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F. Keivanian, N. Mehrshad, S. H. Zahiri, Optimum Layout of Multiplexer with Minimal Average Power based on IWO, Fuzzy-IWO, GA, and Fuzzy GA, ACSIJ Advances in Computer Science : an International Journal, Vol. 3 , Issue 5, No. 11. (2014)
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]
A New Dynamic Simulated Annealing Algorithm for Global Optimization
A New Dynamic Simulated Annealing Algorithm for Global Optimization
en
en
Many problems in system analysis in real world lead to continuous-domain optimization. Existence of sophisticated and many-variable problems in this field emerge need of efficient optimization methods. One of the optimization algorithms for multi-dimensional functions is simulated annealing (SA). In this paper, a modified simulated annealing named Dynamic Simulated Annealing (DSA) is proposed which dynamically switch between two types of generating function on traversed path of continuous Markov chain. Our experiments indicate that this approach can improve convergence and stability and avoid delusive areas in benchmark functions better than SA without any extra mentionable computational cost.
16
23
Hasan
Yarmohamadi
Jahanshah
Kabudian
Seyed Hanif
Mirhosseini
Continuous Global Optimization
Dynamic Simulated Annealing.
Article.2.pdf
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]
Designing an Intelligent System for Diagnosing Diabetes with the Help of the Xcsla System
Designing an Intelligent System for Diagnosing Diabetes with the Help of the Xcsla System
en
en
An intelligent method for diagnosing diabetes is introduced in this article. One of the main problems involved in this disease is that it is not diagnosed correctly and in time and, due to the destructive effects of the progression of the disease on the human body, the need for its timely prediction and diagnosis is felt more than ever before. At present, doctors diagnose diabetes based on documents, scientific tests, and their own experience. However, considering the huge number of patients, a decision support system for recognizing the disease pattern in diabetics can be used. Results of Program Implementation Document (PID) on databases indicated the higher efficiency of the proposed method in diagnosing diabetes compared to the classic XCS system, the ELMAN neural network, SVM clustering, KNN, C4.5, and AD Tree.
24
32
Ehsan
Sadeghipour
Ahmad
Hatam
Farzad
Hosseinzadeh
Decision support system
blood sugar
processing
intelligent system
artificial intelligence
Article.3.pdf
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Seyyed Ehsan Tahami, S. M. Bamshaki, M. A. Khalilzadeh, Diagnosing Diabetes Type I by using the ANFIS, GA-NN algorithm, The First Joint Conference of Intelligent Systems and Fuzzy Systems, Ferdowsi University of Mashhad (2007)
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M. Shariat Panahi, N. Moshtaghi Yazdani, An Improved XCSR Classifier System for Data Mining with Limited Training Samples, Global Journal of Science, Engineering and Technology, (ISSN: 2322-2441), Issue 2 (2012), 52-57
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Navid Moshtaghi Yazdani, A. Yazdani, Improving the XCS algorithm by Using Learning Automata, The International Computer, Information Technology and Digital Media Conference, (2013)
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]
An Expert Clinical System for Diagnosing Obstructive Sleep Apnea with Help From the Xcsr Classifier
An Expert Clinical System for Diagnosing Obstructive Sleep Apnea with Help From the Xcsr Classifier
en
en
Obstructive sleep apnea is a common condition with serious neural-psychological complications and cardiovascular problems if not diagnosed and treated in time. Despite the importance of this disease in our country, it has not received much attention and there are few centers for evaluating patients suffering from it. In this article, an intelligent method is introduced for diagnosing obstructive sleep apnea that uses features extracted from changes in heart rate and respiratory signals in the ECG as input for training and testing the modified XCS classifier system. Comparison of results obtained from implementing the mentioned method with those of other methods on physionet database showed desirable performance and high accuracy of the proposed system in diagnosing obstructive sleep apnea.
33
41
Ehsan
Sadeghipour
Ahmad
Hatam
Farzad
Hosseinzadeh
Apnea
fuzzy neural network
SVM
KNN
XCSR
Article.4.pdf
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M. Shariat Panahi, N. Moshtaghi Yazdani, An Improved XCSR Classifier System for Data Mining with Limited Training Samples, Global Journal of Science, Engineering and Technology, (ISSN: 2322-2441), Issue 2 (2012), 52-57
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An Improved Model of Brain Emotional Learning Algorithm Based on Interval Knowledge
An Improved Model of Brain Emotional Learning Algorithm Based on Interval Knowledge
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en
The brain emotional learning algorithm inspired by a reduced system of a computational model simulates the brain learning performance quite simply with mimicking mammalian brains. The present paper endeavors to come forward to an improved model of the emotional learning algorithm based on the interval knowledge. In this proposed model, based on the interval knowledge, the weights of the amygdala and orbitofrontal sections will be updated. Eventually, the results of implementing and performing the improved brain emotional learning algorithm will be presented to be compared with the original version of the algorithm to Prediction the chaotic time series, Lorenz and Rossler, about which a noticeable improvement in its precision, accuracy and speed of convergence of the final results is reported.
42
53
Yousef
Sharafi
Saeed
Setayeshi
Alireza
Falahiazar
Brain Emotional Learning Algorithm
Interval Knowledge
Chaotic Time Series
Prediction
Article.5.pdf
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Predicting Behaviors of Insurance Costumers by Using the Genetic Algorithm
Predicting Behaviors of Insurance Costumers by Using the Genetic Algorithm
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en
Improving customers’ satisfaction, retaining loyal customers, and acquiring new customers are the most important goals of companies. Because of existing competition conditions among insurance industries, especially in optional insurances such as vehicle body insurance, implementing policies which have benefits for both customers and insurance companies can increase the corporation profitability and customers’ satisfaction. In this paper, general policies of vehicle body insurance in Iran are investigated. In this way, a non-linear function \((C_j)\) is proposed to separate profitable and harmful customers. This function is based on some insurance features of customers such as age of a vehicle, vehicle price, vehicle type, and number of years without accident. Customers are categorized based on the calculated values of \((C_j)\) in which all parameters are optimally set. The parameters have to be set in the training phase to calculate reliable \((C_j)\) values for customers. Parameters of \((C_j)\) are set so that in a subset of the customers of the training set for which the value of \((C_j)\) is greater than a threshold \((Th)\), the number of profitable customers and disadvantageous customers should be maximized and minimized, respectively. A customer with positive \(M\) (monetory) value is a profitable customer. To optimize this multivariable problem with large number of parameters, the genetic algorithm is used. After optimizing the parameters in the training phase by using GA, the behaviors of the customers in the test set are predicted. In the prediction phase, it was estimated that a costumer is profitable or not, even he had accidents and received recompenses from the company. If a customer is predicted as profitable, the company allows him to enjoy some discounts. In the current Iranian insurance policies, all discounts are canceled for next insurance year if a customer has accidents and gives recompense from the company. By implementing the proposed policy, the customers’ satisfactions are increased and also more new customers are acquired from other insurance companies.
54
70
Fatemeh
Bagheri
Majid
Ziaratban
Mohammad Jafar
Tarokh
Behavior prediction
insurance customers
genetic algorithm.
Article.6.pdf
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M. Eslami, J. Vahidi, M. Askarzadeh, Designing and Implementing a Distributed Genetic Algorithm for Optimizing Work Modes in Wireless Sensor Network, Journal of mathematics and computer Science , 11 (2014), 291-299
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On Estimation of Parameters of Lagrangian Katz Distribution
On Estimation of Parameters of Lagrangian Katz Distribution
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en
We have provided a generalized form of improved estimators by promoting usual estimators included in Lagrangian Katz distribution under a weighted squared error loss. As there are several forms derives of this distribution, the results can be employed for other distributions of this family, as well.
71
76
N.
Abbasi
Y.
Najmi
Improved estimator
Lagrangian Katz
Risk function
weighted squared error loss function.
Article.7.pdf
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Computational Model of Social Intelligence Based on Emotional Learning in the Amygdala
Computational Model of Social Intelligence Based on Emotional Learning in the Amygdala
en
en
Today, neural systems are being applied as a powerful tool to solve a host of problems. The more computational model of the presented neural system and the neural system of animals look alike, the more powerful the computational model would be. The brain emotional learning algorithm in the Amygdala inspired by Limbic of mammalian brains was put forward originally by Mor´en. A combination of some Limbic systems as some living creatures will be applied in this paper. The gathering of these living creatures and output summation of each Limbic system in accordance with the relevant applied coefficient will result in social intelligence. The proposed model has been compared with feed-forward back propagation network, Elman back propagation network, and a computational model of emotional learning in the Amygdala to forecast Mackey-Glass Chaotic Time Series. The results of the above mentioned comparison concludes that in forecasting Mackey-Glass Chaotic Time Series, the computational model of social intelligence based on the emotional learning in the Amygdala has shown fewer errors in not only training patterns but also testing patterns.
77
86
Alireza
Falahiazar
Saeed
Setayeshi
Yousef
Sharafi
Machine Intelligence
Social Intelligence
Emotional Learning in the Amygdala
Mackey-Glass Chaotic Time Series
Article.8.pdf
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