Nonlinear system identification using Takagi-Sugeno type neuro-fuzzy model

Panchariya, P. C.; Palit, A.K.; Popović, D.; Sharrna, A.L.

doi:10.1109/is.2004.1344640

Cited by 11 publications

(8 citation statements)

References 19 publications

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“…1,3,14,15 The LNF models can deal with complex identification problems by dividing them into smaller and simpler sub-problems. In fact, the main advantage of the LNF models is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process.…”

Section: A Brief Review On Local Neuro-fuzzy Modelsmentioning

confidence: 99%

“…2,16 The ability to describe different operating regimes of a system or process by different local models (LMs) has made LMNs appealing to many researchers to date. [14][15][16] The pioneering local linear neuro-fuzzy (LLNF) model, originally proposed by Nelles, 1 is one of the most popular LNF models for identification and prediction of nonlinear systems. The LLNF model is trained by local linear model tree (LOLIMOT) learning algorithm, which is based on a divide-and-conquer strategy.…”

Section: A Brief Review On Local Neuro-fuzzy Modelsmentioning

confidence: 99%

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A Developed Local Polynomial Neuro-Fuzzy Model for Nonlinear System Identification

Abdollahzade

Kazemi

2015

Int. J. Artif. Intell. Tools

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This paper proposes a local polynomial model tree (LPMT) learning technique for prediction and identification of nonlinear systems and processes. The proposed LPMT learning algorithm is applied to a local polynomial neuro fuzzy (LPNF) model, which includes local polynomial models (LPMs), as Taylor series expansion of any unknown function. . The LPMT algorithm is established based on the concept of hierarchical binary tree and heuristically partitions the input space into smaller subdomains through axis-orthogonal splits. The proposed learning algorithm starts from a single local polynomial model and then refines the LPNF model by increasing the degree of the worstperforming LPM or by further partitioning of the input space. During the learning procedure, the validity functions automatically form a partition of unity and therefore normalization side effects, e.g. reactivation and poor extrapolation performance, are prevented. The LPNF model, trained by the proposed LPMT algorithm, is used for prediction and identification of the nonlinear processes and systems in three case studies. The obtained results and comparisons to other methods revealed the promising performance of the proposed model.

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Section: A Brief Review On Local Neuro-fuzzy Modelsmentioning

confidence: 99%

Section: A Brief Review On Local Neuro-fuzzy Modelsmentioning

confidence: 99%

A Developed Local Polynomial Neuro-Fuzzy Model for Nonlinear System Identification

Abdollahzade

Kazemi

2015

Int. J. Artif. Intell. Tools

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“…Each neuron in this layer generates a multilinear output which represents the firing strength of a rule; each multilinear term consists of the product of n linear terms as in Eq. (6). The rules with non-zero firing strength are only those associated with the active neurons in layer 1, that is, 2 n active rules.…”

Section: Multi-variable Anfismentioning

confidence: 99%

“…, with x 1 , x 2 and x 3 in [1,6]. In this experiment a total of 1331 (11 3 ) training points and another 1000 intermediate test points have been collected.…”

Section: Three-variablesmentioning

confidence: 99%

“…Fuzzy systems provide a framework to represent imprecise information and to reason with this kind of information, while neural networks enhance fuzzy systems with the capability of learning from input-output data; learning is used to adapt parameters of the fuzzy system as membership functions or rules. Some example representative application areas of NFS are: pattern recognition [1,2], robotics [3,4], nonlinear system identification [5,6], adaptive signal processing [7,8], etc.. In addition, new potential applications can be found in the field of ubiquitous computing and ambient intelligence [9].…”

Section: Introductionmentioning

confidence: 99%

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Modelizing a non-linear system: a computational effcient adaptive neuro-fuzzy system tool based on matlab

Bosque

Campo

Echanobe

2014

Journal of Advanced Computer Science &Amp; Technology

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In a great diversity of knowledge areas, the variables that are involved in the behavior of a complex system, perform normally, a non-linear system. The search of a function that express those behavior, requires techniques as mathematics optimization techniques or others. The new paradigms introduced in the soft computing, as fuzzy logic, neuronal networks, genetics algorithms and the fusion of them like the neuro-fuzzy systems, and so on, represent a new point of view to deal this kind of problems due to the approximation properties of those systems (universal approximators).This work shows a methodology to develop a tool based on a neuro-fuzzy system of ANFIS (Adaptive NeuroFuzzy Inference System) type with piecewise multilinear (PWM) behaviour (introducing some restrictions on the membership functions -triangular-chosen in the ANFIS system). The obtained tool is named PWM-ANFIS Tool, that allows modelize a n-dimensional system with one output and, also, permits a comparison between the neurofuzzy system modelized, a purely PWM-ANFIS model, with a generic ANFIS (Gaussian membership functions) modelized with the same tool. The proposed tool is an efficient tool to deal non-linearly complicated systems.

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Implementation of Adaptive Neuro-Fuzzy Inference System in Fault Location Estimation

Abdullah¹,

Banmongkol

Hoonchareon

et al. 2016

9th International Conference on Robotic, Vision, Signal Processing and Power Applications

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Nonlinear system identification using Takagi-Sugeno type neuro-fuzzy model

Cited by 11 publications

References 19 publications

A Developed Local Polynomial Neuro-Fuzzy Model for Nonlinear System Identification

A Developed Local Polynomial Neuro-Fuzzy Model for Nonlinear System Identification

Modelizing a non-linear system: a computational effcient adaptive neuro-fuzzy system tool based on matlab

Implementation of Adaptive Neuro-Fuzzy Inference System in Fault Location Estimation

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