scholarly journals Assessment of a Takagi–Sugeno-Kang fuzzy model assembly for examination of polyphasic loglinear allometry

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8173
Author(s):  
Hector A. Echavarria-Heras ◽  
Juan R. Castro-Rodriguez ◽  
Cecilia Leal-Ramirez ◽  
Enrique Villa-Diharce
Keyword(s):  
Nonlinear Regression ◽  
Fuzzy Model ◽  
Recursive Least Squares ◽  
Subtractive Clustering ◽  
Membership Functions ◽  
Log Transformation ◽  
Firing Strength ◽  
Break Points ◽  
Takagi Sugeno ◽  
Geometrical Space

Background The traditional allometric analysis relies on log- transformation to contemplate linear regression in geometrical space then retransforming to get Huxley’s model of simple allometry. Views assert this induces bias endorsing multi-parameter complex allometry forms and nonlinear regression in arithmetical scales. Defenders of traditional approach deem it necessary since generally organismal growth is essentially multiplicative. Then keeping allometry as originally envisioned by Huxley requires a paradigm of polyphasic loglinear allometry. A Takagi-Sugeno-Kang fuzzy model assembles a mixture of weighted sub models. This allows direct identification of break points for transition between phases. Then, this paradigm is seamlessly appropriate for efficient allometric examination of polyphasic loglinear allometry patterns. Here, we explore its suitability. Methods Present fuzzy model embraces firing strength weights from Gaussian membership functions and linear consequents. Weights are identified by subtractive clustering and consequents through recursive least squares or maximum likelihood. Intersection of firing strength factors set criterion to estimate breakpoints. A multi-parameter complex allometry model follows by adapting firing strengths by composite membership functions and linear consequents in arithmetical space. Results Takagi-Sugeno-Kang surrogates adapted complexity depending on analyzed data set. Retransformation results conveyed reproducibility strength of similar proxies identified in arithmetical space. Breakpoints were straightforwardly identified. Retransformed form implies complex allometry as a generalization of Huxley’s power model involving covariate depending parameters. Huxley reported a breakpoint in the log–log plot of chela mass vs. body mass of fiddler crabs (Uca pugnax), attributed to a sudden change in relative growth of the chela approximately when crabs reach sexual maturity. G.C. Packard implied this breakpoint as putative. However, according to present fuzzy methods existence of a break point in Huxley’s data could be validated. Conclusions Offered scheme bears reliable analysis of zero intercept allometries based on geometrical space protocols. Endorsed affine structure accommodates either polyphasic or simple allometry if whatever turns required. Interpretation of break points characterizing heterogeneity is intuitive. Analysis can be achieved in an interactive way. This could not have been obtained by relying on customary approaches. Besides, identification of break points in arithmetical scale is straightforward. Present Takagi-Sugeno-Kang arrangement offers a way to overcome the controversy between a school considering a log-transformation necessary and their critics claiming that consistent results can be only obtained through complex allometry models fitted by direct nonlinear regression in the original scales.

A Takagi-Sugeno Fuzzy Dynamic Model of a Concentric-Tubes Heat Exchanger

2009 ◽  
Vol 4 (2) ◽  
Author(s):  
Claudia Ruiz-Mercado ◽  
Arturo Pacheco-Vega ◽  
Gerardo Torres-Chavez
Keyword(s):  
Heat Exchanger ◽  
Cold Water ◽  
Fuzzy Model ◽  
Test Facility ◽  
Subtractive Clustering ◽  
Ts Fuzzy Model ◽  
Linguistic Rules ◽  
Identification Technique ◽  
Takagi Sugeno ◽  
Logic Reasoning

We develop a Takagi-Sugeno (TS) fuzzy model of a concentric-tubes heat exchanger. The model is structured on fuzzy logic reasoning with sets of linguistic rules describing the dynamic characteristics of the thermal system. Using a system identification technique based on adaptive neural networks and subtractive clustering, the fuzzy rules are derived from experimental data of flow rates and fluid temperatures that were previously collected in a heat exchanger test facility. The accuracy of the resulting model is assessed by comparing predictions, versus experimental measurements, of the time-dependent response of the outlet hot- and cold-water temperatures under a step-change in the mass flow rate of the cold fluid. The results indicate that the TS fuzzy model is able to estimate the behavior of the physical system with predicting errors of the order of the experimental uncertainties.

scholarly journals Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3551 ◽  
Author(s):  
Fang Liu ◽  
Ranran Li ◽  
Aliona Dreglea
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Clustering Algorithm ◽  
Fuzzy Model ◽  
Recursive Least Squares ◽  
Shanxi Province ◽  
Support Vector ◽  
Short Term ◽  
Forecasting Method ◽  
Takagi Sugeno

Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can obtain accurate forecasting results though efficient linearization. The proposed method employs meteorological measurements as input. Next, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means clustering algorithm and the recursive least squares method. From these components, the T–S fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve the precision of the short-term wind power forecasting.

Fuzzy Modeling with Local and Global Objectives

1999 ◽  
Vol 3 (5) ◽  
pp. 373-385
Author(s):  
John Yen ◽  
◽  
Wayne Gillespie ◽  
Keyword(s):  
Fuzzy Model ◽  
Current Method ◽  
Training Data ◽  
Local Behavior ◽  
Membership Functions ◽  
Training Set ◽  
Fuzzy Models ◽  
Output Mapping ◽  
Takagi Sugeno ◽  
A Current

Most of the techniques for constructing fuzzy models from data focus only on minimizing the error between the model’s output and the training data; however, these approaches may result in a fuzzy model where individual rules are misleading. The goal of our research is to develop a scheme for identifying Takagi-Sugeno-Kang (TSK) models whose individual rules approximate the training data covered by a single rule, local fitness, while the entire model approximates the whole training set, global fitness. We propose an approach that is a modification of a current method for estimating the consequence portion of a TSK model with predefined membership functions. Then we propose a method for developing membership functions which partition the input space into regions that are more easily modeled in the TSK framework to provide consistent local behavior for all the rules of the model. This approach ensures that a TSK model constructed not only approximates the input-output mapping relationship in the data, but also captures insights about the local behavior of the model.

scholarly journals Potential of Hybrid Adaptive Neuro Fuzzy Model in Simulating Clostridium Difficile Infection Status

2020 ◽  
Vol 3 (1) ◽  
pp. 1-6
Keyword(s):  
Clostridium Difficile ◽  
Fuzzy Model ◽  
The United States ◽  
University Hospital ◽  
Subtractive Clustering ◽  
Membership Functions ◽  
Neuro Fuzzy ◽  
The Status ◽  
Stool Samples ◽  
And Training

The global burden posed by nosocomial diarrhea lead to the strong given attention by health practitioners science its morbidity and mortality rate hit about 500,000 rates annually in the United states. Diagnostic measures have been put in place to detect the presence of CD using different methods. Reliable prediction of the health status of patients is of paramount importance. This study aimed at investigating the status of stool samples collected to test the presence of clostridium difficile as either positive or negative from both inpatient and outpatient from the record units of Near East University Hospital using hybrid adaptive neuro fuzzy (known as ANFIS) model consisting of various combinations of membership functions and training Fis. In this context, the age of the patients, gender, results of the analysis conducted, the department in which the patient was admitted, the age category and the hospitalization were employed as the input variables. The performance accuracy of these membership functions and training FIS combinations were checked using two performance indices determination coefficient (R2) and mean square error (MSE). The obtained computation data driven models proves the reliability of the combination of subtractive clustering membership function and hybrid training FIS over the other three ANFIS combinations. Overall, the results indicated the reliability and satisfaction of hybrid adaptive neuro fuzzy in checking the status of stool samples collected to test the presence of clostridium difficile as either positive or negative from both inpatient and outpatient.

scholarly journals Design of PDC Controllers by Matrix Reversibility for Synchronization ofYinandYangChaotic Takagi-Sugeno Fuzzy Henon Maps

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Chun-Yen Ho ◽  
Hsien-Keng Chen ◽  
Zheng-Ming Ge
Keyword(s):  
Chaos Synchronization ◽  
Matrix Theory ◽  
Chinese Philosophy ◽  
Fuzzy Model ◽  
Invertible Matrix ◽  
Henon Maps ◽  
Hénon Maps ◽  
Feminine Principle ◽  
Simulation Results ◽  
Takagi Sugeno

This paper investigates the synchronization ofYinandYangchaotic T-S fuzzy Henon maps via PDC controllers. Based on the Chinese philosophy,Yinis the decreasing, negative, historical, or feminine principle in nature, whileYangis the increasing, positive, contemporary, or masculine principle in nature.YinandYangare two fundamental opposites in Chinese philosophy. The Henon map is an invertible map; so the Henon maps with increasing and decreasing argument can be called theYangandYinHenon maps, respectively. Chaos synchronization ofYinandYangT-S fuzzy Henon maps is achieved by PDC controllers. The design of PDC controllers is based on the linear invertible matrix theory. The T-S fuzzy model ofYinandYangHenon maps and the design of PDC controllers are novel, and the simulation results show that the approach is effective.

scholarly journals Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Actuator Saturation ◽  
Design Method ◽  
Fuzzy Model ◽  
The Stability ◽  
Interval Type ◽  
Takagi Sugeno

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

Fuzzy observer–based disturbance rejection control for nonlinear fractional-order systems with time-varying delay

2021 ◽  
pp. 107754632110069
Author(s):  
Parvin Mahmoudabadi ◽  
Mahsan Tavakoli-Kakhki
Keyword(s):  
Fractional Order ◽  
Disturbance Rejection ◽  
State Feedback ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Delayed Systems ◽  
Fuzzy Observer ◽  
Time Varying Delay ◽  
Takagi Sugeno

In this article, a Takagi–Sugeno fuzzy model is applied to deal with the problem of observer-based control design for nonlinear time-delayed systems with fractional-order [Formula: see text]. By applying the Lyapunov–Krasovskii method, a fuzzy observer–based controller is established to stabilize the time-delayed fractional-order Takagi–Sugeno fuzzy model. Also, the problem of disturbance rejection for the addressed systems is studied via the state-feedback method in the form of a parallel distributed compensation approach. Furthermore, sufficient conditions for the existence of state-feedback gains and observer gains are achieved in the terms of linear matrix inequalities. Finally, two numerical examples are simulated for the validation of the presented methods.

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