6 results
Search Results
2. A New Branch and Reduce Approach for Solving Generalized Linear Fractional Programming.
- Author
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Yong-Hong Zhang and Chun-Feng Wang
- Subjects
- *
FRACTIONAL programming , *MATHEMATICAL sequences , *ALGORITHMS , *STOCHASTIC convergence , *PROBLEM solving - Abstract
In this paper, for solving generalized linear fractional programming (GLFP), a new branch-and-reduce approach is presented. Firstly, an equivalent problem (EP) of GLFP is given; then, a new linear relaxation technique is proposed; finally, the problem EP is reduced to a sequence of linear programming problems by using the new linear relaxation technique. Meanwhile, to improve the convergence speed of our algorithm, two reducing techniques are presented. The proposed algorithm is proved to be convergent, and some experiments are provided to show its feasibility and efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2017
3. Convergence of Elitist Clonal Selection Algorithm Based on Martingale Theory.
- Author
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Lu Hong and Kamruzzaman, Joarder
- Subjects
- *
STOCHASTIC convergence , *CLONAL selection theory , *ALGORITHMS , *MARTINGALES (Mathematics) , *MARKOV processes , *PROOF theory , *PROBABILITY theory - Abstract
In recent years, progress has been made in the analysis of global convergence of clonal selection algorithms (CSA), but most analyses are based on the theory of Markov chain, which depend on the description of the transition matrix and eigenvalues. However, such analyses are very complicated, especially when the population size is large, and are presented for particular implementations of CSA. In this paper, instead of the traditional Markov chain theory, we introduce martingale theory to prove the convergence of a class of CSA, called elitist clonal selection algorithm (ECSA). Using the submartingale convergence theorem, the best individual affinity evolutionary sequence is described as a submartingale, and the almost everywhere convergence of ECSA is derived. Particularly, the algorithm is proved convergent with probability 1 in finite steps when the state space of population is finite. This new proof of global convergence analysis of ECSA is more simplified and effective, and not implementation specific. [ABSTRACT FROM AUTHOR]
- Published
- 2013
4. A New Algorithm with Low Complexity for Adaptive Filtering.
- Author
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Arezki, M., Benallal, A., Meyrueis, P., and Berkani, D.
- Subjects
- *
ALGORITHMS , *STOCHASTIC convergence , *ELECTRIC filters , *COMPUTATIONAL complexity , *ELECTRONIC data processing - Abstract
In this paper, we propose a new algorithm M-SMFTF for adaptive filtering with fast convergence and low complexity. It is the result of a simplified FTF type algorithm, where the adaptation gain is obtained only from the forward prediction variables and using a new recursive method to compute the likelihood variable. The computational complexity was reduced from 7L to 6L, where L is the finite impulse response filter length. Furthermore, this computational complexity can be significantly reduced to (2L+4P) when used with a reduced P-size forward predictor. This algorithm presents a certain interest, for the adaptation of very long filters, like those used in the problems of echo acoustic cancellation, due to its reduced complexity, its numerical stability and its convergence in the presence of the speech signal. The proposed algorithm outperforms the classical adaptive algorithms because of its convergence speed which approaches that of the RLS algorithm and its computational complexity which is slightly greater than the one of the normalized LMS algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2010
5. Nonlinear Channel Equalization Using A Novel Recurrent Interval Type-2 Fuzzy Neural System.
- Author
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Ching-Hung Lee, Tzu-Wei Hu, and Hao-Han Chang
- Subjects
- *
INTERNET , *ARTIFICIAL neural networks , *NONLINEAR control theory , *FUZZY logic , *LYAPUNOV functions , *STOCHASTIC convergence , *ALGORITHMS - Abstract
Nonlinear inter-symbol interference leads to significant error rate in nonlinear communication and digital storage channel. In this paper, therefore, a novel recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A) is proposed for nonlinear channel equalization. The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the fuzzy logic system in a five-layer neural network structure. The RT2FNN-A is an extensive results of type-2 fuzzy neural network to provide memory elements for capturing the system's dynamic information and has the properties of high approximation accuracy and small network structure. Based on the Lyapunov theorem and gradient descent method, the convergence of RT2FNN-A is guaranteed and the corresponding learning algorithm is derived. In addition, the RT2FNN-A is applied in the nonlinear channel equalization to show the performance and effectiveness of RT2FNN-A system. [ABSTRACT FROM AUTHOR]
- Published
- 2009
6. A New Deformable Model Based on Level Sets for Medical Image Segmentation.
- Author
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Jayadevappa, D., Kumar, S. Srinivas, and Murty, D. S.
- Subjects
DIAGNOSIS ,IMAGING systems ,COMPARATIVE studies ,LEVEL set methods ,ALGORITHMS ,STOCHASTIC convergence - Abstract
This paper presents a new deformable model based on level sets for medical image segmentation which plays a pivotal role in medical diagnosis. The current popular Image segmentation deformable models such as Snakes, Geometric Active Contours, Gradient Vector Flow, Level sets and Variational Level sets have a limitation that the convergence of the contour towards the object boundary is slow and hence not suitable for real time medical diagnosis. To counter this limitation we present an improved image segmentation algorithm which is computationally efficient and also the proximity of the contour towards the object is higher compared to existing algorithms. A new speed term is introduced in the evolution step of variational level set in order to speed up the convergence process. The variational level sets in images with intensity inhomogeneity, tend to be slower and prone to leakage of contour outside the object boundary. This is due to the selection of gradient information for the termination of convergence process. However, this limitation is overcome in the proposed algorithm by modifying the edge indicator function embedded with the speed term that optimizes the effective distance of the attractive force. Experimental results are provided using real time medical images. Comparative tables and graphs highlighting the performance of various deformable models are also presented. [ABSTRACT FROM AUTHOR]
- Published
- 2009
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