• 제목/요약/키워드: Probability-Based Algorithms

검색결과 289건 처리시간 0.03초

Maximization of Zero-Error Probability for Adaptive Channel Equalization

  • Kim, Nam-Yong;Jeong, Kyu-Hwa;Yang, Liuqing
    • Journal of Communications and Networks
    • /
    • 제12권5호
    • /
    • pp.459-465
    • /
    • 2010
  • A new blind equalization algorithm that is based on maximizing the probability that the constant modulus errors concentrate near zero is proposed. The cost function of the proposed algorithm is to maximize the probability that the equalizer output power is equal to the constant modulus of the transmitted symbols. Two blind information-theoretic learning (ITL) algorithms based on constant modulus error signals are also introduced: One for minimizing the Euclidean probability density function distance and the other for minimizing the constant modulus error entropy. The relations between the algorithms and their characteristics are investigated, and their performance is compared and analyzed through simulations in multi-path channel environments. The proposed algorithm has a lower computational complexity and a faster convergence speed than the other ITL algorithms that are based on a constant modulus error. The error samples of the proposed blind algorithm exhibit more concentrated density functions and superior error rate performance in severe multi-path channel environments when compared with the other algorithms.

Improvement of Self Organizing Maps using Gap Statistic and Probability Distribution

  • Jun, Sung-Hae
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • 제8권2호
    • /
    • pp.116-120
    • /
    • 2008
  • Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

Contention-based Reservation MAC Protocol for Burst Traffic in Wireless Packet Networks

  • Lim, In-Taek
    • Journal of information and communication convergence engineering
    • /
    • 제5권2호
    • /
    • pp.93-97
    • /
    • 2007
  • In this paper, centralized access control and slot allocation algorithm is proposed for wireless networks. The proposed algorithm is characterized by the contention-based reservation. In order to reduce the collision probability of reservation request, the base station calculates and broadcasts the transmission probability of reservation requests, and the wireless terminal transmits its reservation request with the received transmission probability. The scheduler allocates the uplink data slots based on the successful reservation requests. Simulation results show that the proposed algorithms can provide high channel utilization, and furthermore, maintains constant delay performance in the heavy traffic environment.

Rank-based Control of Mutation Probability for Genetic Algorithms

  • Jung, Sung-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • 제10권2호
    • /
    • pp.146-151
    • /
    • 2010
  • This paper proposes a rank-based control method of mutation probability for improving the performances of genetic algorithms (GAs). In order to improve the performances of GAs, GAs should not fall into premature convergence phenomena and should also be able to easily get out of the phenomena when GAs fall into the phenomena without destroying good individuals. For this, it is important to keep diversity of individuals and to keep good individuals. If a method for keeping diversity, however, is not elaborately devised, then good individuals are also destroyed. We should devise a method that keeps diversity of individuals and also keeps good individuals at the same time. To achieve these two objectives, we introduce a rank-based control method of mutation probability in this paper. We set high mutation probabilities to lowly ranked individuals not to fall into premature convergence phenomena by keeping diversity and low mutation probabilities to highly ranked individuals not to destroy good individuals. We experimented our method with typical four function optimization problems in order to measure the performances of our method. It was found from extensive experiments that the proposed rank-based control method could accelerate the GAs considerably.

Fuzzy Relaxation Based on the Theory of Possibility and FAM

  • Uam, Tae-Uk;Park, Yang-Woo;Ha, Yeong-Ho
    • Journal of Electrical Engineering and information Science
    • /
    • 제2권5호
    • /
    • pp.72-78
    • /
    • 1997
  • This paper presents a fuzzy relaxation algorithm, which is based on the possibility and FAM instead of he probability and compatibility coefficients used in most of existing probabilistic relaxation algorithms, Because of eliminating stages for estimating of compatibility coefficients and normalization of the probability estimates, the proposed fuzzy relaxation algorithms increases the parallelism and has a simple iteration scheme. The construction of fuzzy relaxation scheme consists of the following three tasks: (1) definition of in/output linguistic variables, their term sets, and possibility. (2) Definition of FAM rule bases for relaxation using fuzzy compound relations. (3) Construction of the iteration scheme for calculating the new possibility estimate. Applications to region segmentation an ege detectiojn algorithms show that he proposed method can be used for not only reducing the image ambiguity and segmentation errors, but also enhancing the raw edge iteratively.

  • PDF

Performance Improvement on MPLS On-line Routing Algorithm for Dynamic Unbalanced Traffic Load

  • Sa-Ngiamsak, Wisitsak;Sombatsakulkit, Ekanun;Varakulsiripunth, Ruttikorn
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 제어로봇시스템학회 2005년도 ICCAS
    • /
    • pp.1846-1850
    • /
    • 2005
  • This paper presents a constrained-based routing (CBR) algorithm called, Dynamic Possible Path per Link (D-PPL) routing algorithm, for MultiProtocol Label Switching (MPLS) networks. In MPLS on-line routing, future traffics are unknown and network resource is limited. Therefore many routing algorithms such as Minimum Hop Algorithm (MHA), Widest Shortest Path (WSP), Dynamic Link Weight (DLW), Minimum Interference Routing Algorithm (MIRA), Profiled-Based Routing (PBR), Possible Path per Link (PPL) and Residual bandwidth integrated - Possible Path per Link (R-PPL) are proposed in order to improve network throughput and reduce rejection probability. MIRA is the first algorithm that introduces interference level avoidance between source-destination node pairs by integrating topology information or address of source-destination node pairs into the routing calculation. From its results, MIRA improves lower rejection probability performance. Nevertheless, MIRA suffer from its high routing complexity which could be considered as NP-Complete problem. In PBR, complexity of on-line routing is reduced comparing to those of MIRA, because link weights are off-line calculated by statistical profile of history traffics. However, because of dynamic of traffic nature, PBR maybe unsuitable for MPLS on-line routing. Also, both PPL and R-PPL routing algorithm we formerly proposed, are algorithms that achieve reduction of interference level among source-destination node pairs, rejection probability and routing complexity. Again, those previously proposed algorithms do not take into account the dynamic nature of traffic load. In fact, future traffics are unknown, but, amount of previous traffic over link can be measured. Therefore, this is the motivation of our proposed algorithm, the D-PPL. The D-PPL algorithm is improved based on the R-PPL routing algorithm by integrating traffic-per-link parameters. The parameters are periodically updated and are dynamically changed depended on current incoming traffic. The D-PPL tries to reserve residual bandwidth to service future request by avoid routing through those high traffic-per-link parameters. We have developed extensive MATLAB simulator to evaluate performance of the D-PPL. From simulation results, the D-PPL improves performance of MPLS on-line routing in terms of rejection probability and total throughput.

  • PDF

Optimization-based method for structural damage detection with consideration of uncertainties- a comparative study

  • Ghiasi, Ramin;Ghasemi, Mohammad Reza
    • Smart Structures and Systems
    • /
    • 제22권5호
    • /
    • pp.561-574
    • /
    • 2018
  • In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

MPLS 트래픽 엔지니어링을 위한 다중경로 Constraint-based 라우팅 알고리즘 (Muli-path Constraint-based Routing Algorithms for MPLS Traffic Engineering)

  • Lee, Jae-Young;Kim, Byung-Chul
    • 한국통신학회논문지
    • /
    • 제29권5B호
    • /
    • pp.508-519
    • /
    • 2004
  • 이 논문에서는 MPLS를 사용한 인터넷 트래픽 엔지니어링에 사용될 수 있는 다중경로 한계조건 기반 라우팅 알고리즘을 제안한다. 기존에 사용되던 한계조건 기반 최단경로 라우팅 알고리즘은 트래픽 엔지니어링의 중요한 요소 중에 하나인 대역폭 한계조건이 큰 간을 가지는 경우에는 조건을 만족하는 경로를 찾지 못할 확률이 높아진다. 제안된 다중경로 알고리즘은 대역폭 한계조건을 만족하는 하나의 경로를 찾을 수 없는 경우, 대역폭 조건을 여러 개의 작은 값으로 나누어 각각의 작은 대역폭 한계조건에 대해 다중경로를 찾는 알고리즘이다. 시뮬레이션을 통하여 제안한 알고리즘을 사용할 경우 주어진 네트웍 상황과 대역폭 한계 조건에서 더 높은 경로설정 성공확률을 나타내며, 네트웍 자원 이용률도 개선되는 것을 보였다.

Blind Algorithms with Decision Feedback based on Zero-Error Probability for Constant Modulus Errors

  • 김남용;강성진
    • 한국통신학회논문지
    • /
    • 제36권12C호
    • /
    • pp.753-758
    • /
    • 2011
  • The constant modulus algorithm (CMA) widely used in blind equalization applications minimizes the averaged power of constant modulus error (CME) defined as the difference between an instant output power and a constant modulus. In this paper, a decision feedback version of the linear blind algorithm based on maximization of the zero-error probability for CME is proposed. The Gaussian kernel of the maximum zero-error criterion is analyzed to have the property to cut out excessive CMEs that may be induced from severely distorted channel characteristics. Decision feedback approach to the maximum zero-error criterion for CME is developed based on the characteristic that the Gaussian kernel suppresses the outliers and this prevents error propagation to some extent. Compared to the linear algorithm based on maximum zero-error probability for CME in the simulation of blind equalization environments, the proposed decision feedback version has superior performance enhancement particularly in cases of severe channel distortions.

Enhanced Pulse Protocol RFID Reader Anti-collision Algorithm using Slot Occupied Probability in Dense Reader Environment

  • Song, In-Chan;Fan, Xiao;Chang, Kyung-Hi
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • 제2권6호
    • /
    • pp.299-311
    • /
    • 2008
  • The Radio Frequency IDentification (RFID) system is a contactless automatic identification system, which comprises readers and tags. In RFID systems, a reader collision occurs when there is interference in communication between one reader and the tags, due to the signals from other readers. The reader collision problem is considered as the fundamental problem affecting high density RFID reader installations. In this paper, we analyze the existing reader anti-collision algorithms. We also propose a pulse protocol-based reader anti-collision algorithm using slot occupied probability (SOP). The implementation of this improvement is simple, yet it effectively mitigates most reader collisions in dense reader mode, as shown in our simulation. That is, the proposed algorithm reduces the identification time, and increasesthe system throughput and system efficiency compared with the conventional reader anti-collision algorithms.