A fast parameter estimation for nonlinear multi-regressions based on the Choquet integral with quantum-behaved particle swarm optimization

Jau, You-Min; Wu, Chia‐Ju; Jeng, Jin-Tsong

doi:10.1007/s10015-010-0790-y

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…For observations without outliers, the MQPSO algorithm offers superior performance for estimating parameters than the GA [ 14 ]. Because the kernel of estimating fitness is the LS estimator, the MQPSO algorithm always makes a serious deviation in the contaminated circumstance.…”

Section: The Lts-mqpso Algorithmmentioning

confidence: 99%

“…However, particles usually fall into local extreme states in multimode optimization systems and then take on the premature phenomenon. In order to make use of the merits of quick convergence and conquer premature in the traditional PSO, we proposed a QPSO algorithm with elitist crossover mechanism of the GA (named MQPSO) in our previous work [ 14 ] and demonstrated a superior performance than the GA in estimations of model parameters. In this paper, we improve the MQPSO algorithm proposed in our previous work to manipulate systems with outliers.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral

Jau

Jeng

2011

Sensors

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Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets; meanwhile, the distributed multi-sensor systems which employ several geographically separated local sensors are required to provide sufficient messages or data with similar and/or dissimilar characteristics. These kinds of information fusion techniques have been widely investigated and used for implementing several information retrieval systems. However, the results obtained from the information fusion systems vary in different situations and performing intelligent aggregation and fusion of information from a distributed multi-source, multi-sensor network is essentially an optimization problem. A flexible and versatile framework which is able to solve complex global optimization problems is a valuable alternative to traditional information fusion. Furthermore, because of the highly dynamic and volatile nature of the information flow, a swift soft computing technique is imperative to satisfy the demands and challenges. In this paper, a nonlinear aggregation based on the Choquet integral (NACI) model is considered for information fusion systems that include outliers under inherent interaction among feature attributes. The estimation of interaction coefficients for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum-behavior (QPSO) and the high breakdown value estimator, least trimmed squares (LTS). From simulation results, the proposed MQPSO algorithm with LTS (named LTS-MQPSO) readily corrects the deviations caused by outliers and swiftly achieves convergence in estimating the parameters of the proposed NACI model for the information fusion systems with outliers.

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Section: The Lts-mqpso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral

Jau

Jeng

2011

Sensors

Self Cite

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“…However, particles usually fall into local extreme state in multimode optimization systems and then take on the premature phenomenon. In order to overcome these defects, we proposed a QPSO algorithm with elitist crossover mechanism of the GA (named MQPSO) in our previous work [13] and performed a superior performance. In this paper, we improve the MQPSO algorithm to manipulate systems with outliers which make serious deviations.…”

Section: Introductionmentioning

confidence: 98%

Modeling of fuzzy integral based nonlinear multi-regressions systems with QPSO-GS

Jau

Jeng

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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A nonlinear multi-regression based on fuzzy integral (NAFI) model that include outliers under inherent interaction among feature attributes is considered in this paper. The modeling of the proposed model is also performed via a modified algorithm base on particle swarm optimization with quantumbehavior (MQPSO) and the high breakdown value estimator, least trimmed squares (LTS). That is, we successfully integrate mechanisms of the genetic algorithm and the simulated annealing into the QPSO algorithm to estimate parameters of the NAFI model; meanwhile, the LTS estimator is also introduced to filter out outliers. From simulation results, the proposed MQPSO algorithm with LTS estimator (named QPSO-GS) readily corrects the deviation caused by outliers and swiftly achieves convergences on estimating the parameters of the proposed NAFI model with outliers.Keywords-Fuzzy integral; nonlinear multi-regression based on fuzzy integral; particle swarm optimization with quantum-behavior; least trimmed squares.

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“…Farzi and Dastjerdi [19] proposed a modified quantum-behaved particle swarm optimization based on adding a leaping behavior to avoid falling into the local optimum. Jau et al [20] proposed a modified QPSO (MQPSO) algorithm based on applying the concept of the GA to improve the convergent speed and conquer the phenomenon of premature. Sun et al [21] proposed a diversity-maintained quantumbehaved particle swarm optimization (DMQPO) algorithm based on the analysis of QPSO and integrates a diversity control strategy to enhance the global search ability of the particle swarm.…”

Section: Introductionmentioning

confidence: 99%

Research on an Improved Quantum Particle Swarm Optimization and its Application

Wang¹

2016

IJMUE

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The quantum particle swarm optimization (QPSO) algorithm exists some defects, such as premature convergence, poor search ability and easy falling into local optimal solutions. The adaptive adjustment strategy of inertia weight, chaotic search method and neighborhood mutation strategy are introduced into the QPSO algorithm in order to propose an improved quantum particle swarm optimization (AMCQPSO) algorithm in this paper. In the AMCQPSO algorithm, the chaotic search method is employed to promote the quality of initial population. The adaptive adjustment strategy of inertia weight is used to adjust the global search ability and local search ability of particles in the running process of QPSO algorithm. The neighborhood mutation strategy is used to increase the diversity of population and avoid premature convergence. Finally, in order to evaluate the performance of the AMCQPSO algorithm, several well-known benchmark functions are selected in this paper. The experiment simulations show that the proposed AMCQPSO algorithm can effectively improve the quality of solutions, and takes on powerful optimizing ability and more quickly convergence speed.

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A fast parameter estimation for nonlinear multi-regressions based on the Choquet integral with quantum-behaved particle swarm optimization

Cited by 5 publications

References 5 publications

Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral

Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral

Modeling of fuzzy integral based nonlinear multi-regressions systems with QPSO-GS

Research on an Improved Quantum Particle Swarm Optimization and its Application

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