Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

Leng, Gang; McGinnity, T.M.; Prasad, Girijesh

doi:10.1109/tfuzz.2006.877361

Cited by 146 publications

(63 citation statements)

References 43 publications

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“…The approach of using evolutionary computation for learning of fuzzy neural networks is one of the most reliable strategies [27].…”

Section: Related Workmentioning

confidence: 99%

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Saadian

2017

Teh. vjesn.

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Original scientific paper In this paper a new method based on evolutionary clustering and locally linear neuro-fuzzy (LLNF) models is proposed for the problem of object tracking in videos. This approach utilizes clustering on color feature space to obtain a model of object which is given at the initial frame. To achieve the optimal clustering, evolutionary optimization methods are used. Based on the results of clustering, parameters of LLNF model is determined so it can be used as an identifier of object during the real time video streaming. To track the object, a swarm of weighted evolving linear models are used to estimate the location and size of the object at next frame based on its current and previous states. The performance of the proposed method is evaluated on a benchmark data set and compared to other methods performed on the same data set. The results show that the accuracy of the proposed method is superior to previous methods. Keywords: clustering; evolutionary computation; Locally Linear Neuro-Fuzzy model; object tracking; swarm optimization Praćenje predmeta u video snimkama pomoću modela evolucijskog grupiranja i lokalno linearnih neuro-fuzzy modelaIzvorni znanstveni članak U radu se za problem praćenja objekta u video snimkama predlaže nova metoda koja se zasniva na modelima evolucijskog grupiranja i lokalno linearnim neuro-fuzzy modelima (LLNF). Taj pristup koristi grupiranje na prostoru karakteristike u boji kako bi se dobio model predmeta zadanog u početnom okviru. Za postizanje optimalnog grupiranja, primijenjene su metode evolucijske optimizacije. Na temelju rezultata grupiranja određeni su parametri LLNF modela te se on može rabiti kao identifikator objekta tijekom video prijenosa u realnom vremenu. Za praćenje objekta koristi se roj ponderiranih izvedenih linearnih modela za procjenu lokacije i veličine objekta u sljedećem okviru na temelju njegovog postojećeg i prethodnih stanja. Učinkovitost predložene metode procijenjena je na referentnom nizu podataka i uspoređena s drugim metodama provedenim na istom nizu podataka. Rezultati pokazuju da je po točnosti predložena metoda bolja od prethodnih.

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“…The approach of using evolutionary computation for learning of fuzzy neural networks is one of the most reliable strategies [27].…”

Section: Related Workmentioning

confidence: 99%

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Saadian

2017

Teh. vjesn.

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“…Many authors have proposed suitable combinations of fuzzy, genetic and neural techniques for different applications (Leng et al, 2006;Saridakis et al, 2006;Cho, 2002). In this way, hybrid intelligent algorithms have been developed.…”

Section: Jcsmentioning

confidence: 99%

“…In this way, hybrid intelligent algorithms have been developed. For example, a hybrid algorithm based on a genetic algorithm to design a neuro-fuzzy network is proposed in (Leng et al, 2006). In such work, the model has been built for a system without a priori knowledge about the partitions of input space and the number of fuzzy rules.…”

Section: Jcsmentioning

confidence: 99%

“…Khan et al (2008) the membership functions and the fuzzy logic rules were optimized through Genetic Algorithms methods for a temperature control system. Thanks to GA, Leng et al (2006) eliminated the limitation on symmetric membership functions and symmetric fuzzy rules. To achieve better control performances of complex systems, neuro-fuzzy techniques are developed.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Control Algorithms for Second Order Systems

Pelusi¹

2013

Journal of Computer Science

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Proportional Integral Derivative (PID) controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.

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“…In addition, based on exterior environment change, it regulates its structure and changes its function to be adaptive to the change of environment. Three selforganization functions of automorphism, self-feedback and self-adaptation have their various characteristics in FNN control [4][5][6]. However, they are not isolated but mutually related and interacted.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Implementation of Self-Organization Fuzzy Neutral Network Control Algorithm

Zeng¹,

Shi²

2014

TOAUTOCJ

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Traditional fuzzy neural network tends to be used for parameters identification and the network structure is separated by grids, so there are obvious defects in this mode of control design. This paper introduces a flexibly model of self-organizing fuzzy neural network according to specific network structure. We analyze its structure and containing parameters and propose an improved nearest neighbor clustering algorithm first for the predicting model of online identification. For parameter optimization, the parameter value acquired at self-organizing learning phase is adopted as the initial value of supervised learning. Then it adopts BP algorithm to adjust the parameter to optimal value based on the same training set, so as to acquire the final model of FNN. The experiments demonstrate that our algorithm can solve the predicting problems of nonlinear system with constraints, and the range and changing rate of control signal. It shows rapid computing speed, better stability and strong anti-disturbance capacity. It is also verified to be suitable for actual engineering control environments.

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Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

Cited by 146 publications

References 43 publications

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Optimal Control Algorithms for Second Order Systems

Optimization and Implementation of Self-Organization Fuzzy Neutral Network Control Algorithm

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