Comparison of EM algorithm and particle swarm optimisation for local model network training

Hametner, Christoph; Jakubek, Stefan

doi:10.1109/iccis.2010.5518547

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…PSO has been used to train an LMN in [70] due to its potential for finding the global optimum. PSO can be used to find the optimal local model parameters, w i , along with the reduced split position parameters, v * , however, this would unnecessarily increase the search space and consequently slow down the optimization process as the local model parameters can be estimated analytically.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

“…The Expectation-Maximization (EM) algorithm, which has a good convergence [66,67], has been used to optimize the local model network in [68] and to optimize the hierarchical decomposition of the logistic discriminant function in [69]. Furthermore, Particle Swarm Optimization (PSO) algorithm, which can locate global optimum, has been used to optimize the hierarchical decomposition of the logistic discriminant function in [70]. However, these works did not consider the undesired effect of split-parameter optimization on the steepness of the transition between the validity functions.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Mahfuz

Muyeen

2022

Electronics

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Developing a mathematical model has become an inevitable need in studies of all disciplines. With advancements in technology, there is an emerging need to develop complex mathematical models. System identification is a popular way of constructing mathematical models of highly complex processes when an analytical model is not feasible. One of the many model architectures of system identification is to utilize a Local Model Network (LMN). Hierarchical Local Model Tree (HILOMOT) is an iterative LMN training algorithm that uses the axis-oblique split method to divide the input space hierarchically. The split positions of the local models directly influence the accuracy of the entire model. However, finding the best split positions of the local models presents a nonlinear optimization problem. This paper presents an optimized HILOMOT algorithm with enhanced Expectation–Maximization (EM) and Particle Swarm Optimization (PSO) algorithms which includes the normalization parameter and utilizes the reduced-parameter vector. Finally, the performance of the improved HILOMOT algorithm is compared with the existing algorithm by modeling the NOx emission model of a gas turbine and multiple nonlinear test functions of different orders and structures.

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Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Mahfuz

Muyeen

2022

Electronics

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Axes-oblique partitioning strategies for local model networks

Nelles

2006

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006

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Train Fuzzy Cognitive Maps by gradient residual algorithm

Zhang

Shen

Miao

2011

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

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Fuzzy Cognitive Maps (FCM) is a popular technique for describing dynamic systems. A FCM for a dynamic system is a signed graph consisted of relevant concepts and causal relationships/weights between the concepts in the system. With suitable weights defined by experts in the related areas, the inference of the FCM can provide meaningful modeling of the system. Thus correctness of the weights is crucial to the success of a FCM system. Normally the weights are set by experts in the related areas. Considering the possible inefficiency and subjectivity of experts when judging the weights, it is an appealing idea to generate weights automatically according to the samples obtained through observation of the system. Some training algorithms were proposed. However, to our best knowledge, few learning algorithm has been reported to generate weight matrix based on sample sequences with continuous values. In this paper, we introduce a new learning algorithm to train the weights of FCM. In the proposed algorithm, the weights are updated by gradient descent on a squared Bellman residual, which is an accepted method in machine learning. The experiment results show that given sufficient training samples, the correct weights can be approximated by the algorithm. The algorithm proposes a new way for FCM research and applications.

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Comparison of EM algorithm and particle swarm optimisation for local model network training

Cited by 5 publications

References 16 publications

Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Axes-oblique partitioning strategies for local model networks

Train Fuzzy Cognitive Maps by gradient residual algorithm

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