scholarly journals Modeling of Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) for Power Prediction

2021 ◽  
Author(s):  
Jafar Tavoosi ◽  
Amir Abolfazl Suratgar ◽  
Mohammad Bagher Menhaj ◽  
Amir Mosavi ◽  
Ardashir Mohammadzadeh ◽  
...  
Keyword(s):  
Solar Cell ◽  
Wind Turbine ◽  
Fuzzy System ◽  
Back Propagation ◽  
Optimal Number ◽  
Membership Functions ◽  
Back Propagation Algorithm ◽  
Power Prediction ◽  
Type Reduction

Not only does this paper present a novel type-2 fuzzy system for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also it brings forward an exquisite technique to acquire an optimal number of membership functions and the corresponding rules. It proposes a seven-layered NCPRT2FS. For fuzzification in the first two layers, Gaussian type-2 fuzzy membership functions with uncertainty in the mean, are exploited. The third layer comprises rule definition and the forth one embeds fulfillment of type reduction. The three last remained layers are the ones in which resultant left–right firing points, two end-points and output all get assessed correspondingly. It should not be neglected off the nutshell that recurrent feedback at the fifth layer exerts delayed outputs ameliorating efficiency of the suggested NCPRT2FS. Later in the paper, a modern structural learning, established on type-2 fuzzy clustering, is held forth. An adaptively rated learning back-propagation algorithm is extended to adjust the parameters ensuring the convergence as well. Eventually, solar cell photo-voltaic and wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge so persuasive.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

2021 ◽  
pp. 014233122110066
Author(s):  
Yang Chen ◽  
Jiaxiu Yang
Keyword(s):  
Fuzzy Logic ◽  
Back Propagation ◽  
Membership Functions ◽  
Identification Problems ◽  
Fuzzy Logic Systems ◽  
Fuzzy Identification ◽  
Logic Systems ◽  
Academic Topic ◽  
Interval Type

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

scholarly journals An Interval Type-2 Fuzzy System with a Species-Based Hybrid Algorithm for Nonlinear System Control Design

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Chung-Ta Li ◽  
Ching-Hung Lee ◽  
Feng-Yu Chang ◽  
Chih-Min Lin
Keyword(s):  
Computational Complexity ◽  
Network Structure ◽  
Back Propagation ◽  
System Control ◽  
Total Force ◽  
Membership Functions ◽  
Species Determination ◽  
Interval Type ◽  
Force Calculation

We propose a species-based hybrid of the electromagnetism-like mechanism (EM) and back-propagation algorithms (SEMBP) for an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS) design. The interval type-2 asymmetric fuzzy membership functions (IT2 AFMFs) and the TSK-type consequent part are adopted to implement the network structure in AIT2FNS. In addition, the type reduction procedure is integrated into an adaptive network structure to reduce computational complexity. Hence, the AIT2FNS can enhance the approximation accuracy effectively by using less fuzzy rules. The AIT2FNS is trained by the SEMBP algorithm, which contains the steps of uniform initialization, species determination, local search, total force calculation, movement, and evaluation. It combines the advantages of EM and back-propagation (BP) algorithms to attain a faster convergence and a lower computational complexity. The proposed SEMBP algorithm adopts the uniform method (which evenly scatters solution agents over the feasible solution region) and the species technique to improve the algorithm’s ability to find the global optimum. Finally, two illustrative examples of nonlinear systems control are presented to demonstrate the performance and the effectiveness of the proposed AIT2FNS with the SEMBP algorithm.

scholarly journals Adaptive Backstepping Fuzzy Control Based on Type-2 Fuzzy System

2012 ◽  
Vol 2012 ◽  
pp. 1-27 ◽  
Author(s):  
Ll Yi-Min ◽  
Yue Yang ◽  
Li Li
Keyword(s):  
Fuzzy Control ◽  
Fuzzy System ◽  
External Disturbance ◽  
Approximation Error ◽  
Adaptive Backstepping ◽  
Control Approach ◽  
Type Reduction ◽  
Adaptive Laws

A novel indirect adaptive backstepping control approach based on type-2 fuzzy system is developed for a class of nonlinear systems. This approach adopts type-2 fuzzy system instead of type-1 fuzzy system to approximate the unknown functions. With type-reduction, the type-2 fuzzy system is replaced by the average of two type-1 fuzzy systems. Ultimately, the adaptive laws, by means of backstepping design technique, will be developed to adjust the parameters to attenuate the approximation error and external disturbance. According to stability theorem, it is proved that the proposed Type-2 Adaptive Backstepping Fuzzy Control (T2ABFC) approach can guarantee global stability of closed-loop system and ensure all the signals bounded. Compared with existing Type-1 Adaptive Backstepping Fuzzy Control (T1ABFC), as the advantages of handling numerical and linguistic uncertainties, T2ABFC has the potential to produce better performances in many respects, such as stability and resistance to disturbances. Finally, a biological simulation example is provided to illustrate the feasibility of control scheme proposed in this paper.

scholarly journals Development Multiple Neuro-Fuzzy System Using Back-propagation Algorithm

2013 ◽  
Vol 6 (2) ◽  
pp. 794-804
Author(s):  
Dr. Imad S. Alshawi ◽  
Haider Khalaf Allamy ◽  
Dr. Rafiqul Zaman Khan
Keyword(s):  
Fuzzy System ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Back Propagation Algorithm ◽  
New Approach ◽  
Training Time ◽  
Propagation Algorithm ◽  
Neuro Fuzzy ◽  
Highly Nonlinear ◽  
Input Variables

When fuzzy systems are highly nonlinear or include a large number of input variables, the number of fuzzy rules constituting the underlying model is usually large. Dealing with a large-size fuzzy model may face many practical problems in terms of training time, ease of updating, generalizing ability and interpretability. Multiple Fuzzy System (MFS) is one of effective methods to reduce the number of rules, increase the speed to obtain good results. This paper is therefore proposes another approach call Multiple Neuro-Fuzzy System (MNFS) which can further enhance the performance of the MFS approach. The new approach is used Back-propagation algorithm in the learning process. The performance of the proposed approach evaluates and compares with MFS by three experiments on nonlinear functions. Simulation results demonstrate the effectiveness of the new approach than MFS with regards to enhancement of the accuracy of the results.  

Interpolation functions of interval Type-2 fuzzy systems

2021 ◽  
pp. 1-17
Author(s):  
Shan Zhao ◽  
Zhao Li
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Classical Type ◽  
Data Sets ◽  
Single Output ◽  
Type Reduction ◽  
Single Input ◽  
Interval Type ◽  
Interpolation Functions

The interpolation functions of interval type-2 fuzzy systems and their universal approximation are investigated in this paper. Two types of fuzzification methods are designed to construct the antecedents and consequents of the type-2 inference rules. Then the properties of the fuzzy operator and the type-reduction algorithm are used to integrate all parts of the fuzzy system. Interpolation functions of interval type-2 fuzzy systems, which are proved to be universal approximators, are obtained based on three models, namely single input and single output, double inputs and single output, and multiple inputs and single output. The proposed approach is applied to approximate experiments of dynamic systems so as to evaluate the system performance. The system parameters are optimized by the QPSO algorithm. Experimental results for several data sets are given to show the approximation performances of the proposed interpolation functions are better than those of the interpolation function of the classical type-1 fuzzy system.

scholarly journals A novel optimum tip speed ratio control of low speed wind turbine generator based on type-2 fuzzy system

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Muldi Yuhendri ◽  
Mukhlidi Muskhir ◽  
Taali Taali
Keyword(s):  
Control System ◽  
Wind Turbine ◽  
Output Power ◽  
Fuzzy System ◽  
Speed Ratio ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
Tip Speed Ratio ◽  
Speed Controller

Variable speed control of wind turbine generator systems have been developed to get maximum output power at every wind speed variation, also called Maximum Power Points Tracking (MPPT). Generally, MPPT control system consists of MPPT algorithm to track the controller reference and generator speed controller. In this paper, MPPT control system is proposed for low speed wind turbine generator systems (WTGs) with MPPT algorithms based on optimum tip speed ratio (TSR) and generator speed controller based on field oriented control using type-2 fuzzy system (T2FS). The WTGs are designed using horizontal axis wind turbines to drive permanent magnet synchronous generators (PMSG). The simulation show that the MPPT system based optimum TSR has been able to control the generator output power around the maximum point at all wind speeds.

scholarly journals Uncertainty Avoider Interval Type II Defuzzification Method

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Sadegh Aminifar
Keyword(s):  
Fuzzy System ◽  
Uncertainty Avoidance ◽  
System Response ◽  
Type Reduction ◽  
Meaningful Relationship ◽  
System Output ◽  
Computational Bottleneck ◽  
Interval Type

One of the IT2FS (interval type-2 fuzzy system) defuzzification methods uses the iterative KM algorithm. Because of the iterative nature of KM-type reduction, it may be a computational bottleneck for the real-time applications of IT2FSs. There are several other interval type-2 defuzzification methods suffering from lack of meaningful relationship between membership function uncertainties and changing of system output due to lack of clearly defined variables related to uncertainty in their methods. In this paper, a new approach for IT2FS defuzzification is presented by reconfiguring interval type-2 fuzzy sets and how uncertainties are present in them. This closed-formula method provides meaningful relation between the presence of uncertainty and its effect on system output. This study investigates uncertainty avoidance that the output of IT2FS obtained by centroid or bisection methods in comparison with type-1 fuzzy system (T1FLS) moves to points with less uncertainty. Uncertainty can enter into T1FSs and affect system response. Finally, for proving the affectivity of the proposed defuzzification method and uncertainty avoidance, several investigations are done and a prototype two-input one-output IT2FS MATLAB code is enclosed.

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