Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification

Haznedar, Bülent

doi:10.18201/ijisae.266053

Cited by 30 publications

(19 citation statements)

References 13 publications

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“…The focus of research put forward the model of Adaptive Neuro Fuzzy Inference System (ANFIS) for perpetrators maker Bid auction in PT Garuda Karya Mandiri (GKM) to create the results predicted light price is right and not hurt the company. ANFIS is a network model which Sugeno-type fuzzy system is combined with neural learning ability [4]. This research includes applied research.…”

Section: Methodsmentioning

confidence: 99%

Prediction Of Lamp Price Using Adaptive Neuro Fuzzy Inference System

Ridwan¹

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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Precise Lamp predictions lead to the optimal allocation of resources, efficiency improvements, and increased company revenue. to forecast the price of building lights is a type of non-linear prediction. In this study, periodic data with time range of data from Jan-2009 to Dec-2015 used as prediction process parameter using ANFIS. The research method was started by mapping data into 4 input parameters and 1 output result, followed by training and testing 3 times with data classification period 1-19, 2-20, and 3-21. The result of the research Prediction of price using ANFIS model based on 63 data with 3 variant of light and input parameter that include number of membership function = 2, membership function type Trapezium, error goal 0, and epoch Maximum 100 able to give value of RMSE=0.05, MAPE=5.62, and produces predictive price according to actual data with percentage difference of 1,65x10-3.

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Section: Methodsmentioning

confidence: 99%

Prediction Of Lamp Price Using Adaptive Neuro Fuzzy Inference System

Ridwan¹

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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“…GA is an evolutionary heuristic optimization algorithm which is based on population [20,31,[36][37][38]. The process which leads to the generation of new solution in GA has a semblance of the natural selection process of living organisms.…”

Section: Model Optimization Based On Genetic Algorithm (Ga)mentioning

confidence: 99%

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Olatunji

Akinlabi

Madushele

et al. 2020

Lecture Notes in Mechanical Engineering

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The elemental composition of biomass is a significant property, which determines the energy content of biomass feedstock. This article develops a prediction model based on a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized with genetic algorithm (GA). The model inputs were the proximate constituents of biomass which are ash, fixed carbon, and volatile matter. These were used to predict the hydrogen (H), oxygen (O) and carbon (C) content of biomass fuels. The proposed algorithm was evaluated based on some known performance metrics. The root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of correlation (CC), mean absolute error (MAE) are 3.673, 2.4609, 5.1757, 0.9464, 0.309 at computation time (CT) of 33.65 secs for carbon (C); 0.6293, 0.4168, 8.3011, 0.75581, 0.0716 at CT of 40.21 secs for hydrogen (H); 4.4538, 3.1042, 13.3983,0.9167, 0.9899 at CT of 33.57 secs for oxygen (O), respectively. Regression analysis was also carried out to determine the level of dependence among the correlated variables. The model performance shows that GA-ANFIS can be applied in the computation of the elemental composition of biomass for strategic decision-making. Keywords GA-ANFIS • Biomass feedstock • Efficient utilization • Elemental composition 1 Introduction As of 2017 the United Nation, UN projected population growth from 7.6 billion to 9.8 billion by 2050 [1]. The clean and sustainable energy is the viable solution

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“…Turki (2012) [20] used the ANFIS trained by PSO for nonlinear system adaptive control. Haznedar (2016) [21] performed the classification of Liver microarray cancer data with the ANFIS trained using GA. Furthermore, in another study of Haznedar et al (2016) [22], the ANFIS trained by GA was also used in dynamic system identification.…”

Section: Related Workmentioning

confidence: 99%

“…Haznedar (2016) [21] performed the classification of Liver microarray cancer data with the ANFIS trained using GA. Furthermore, in another study of Haznedar et al (2016) [22], the ANFIS trained by GA was also used in dynamic system identification. In their study, Karaboga et al (2014Karaboga et al ( ,2016 [23,24] used the ANFIS trained with Artificial Bee Colony algorithm in nonlinear system identification.…”

Section: Related Workmentioning

confidence: 99%

Training Anfis System with Moth-Flame Optimization Algorithm

Canayaz

2019

ijisae

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Adaptive Neuro Fuzzy Inference System (ANFIS) is an adaptive network that can use the computation and learning abilities of artificial neural network together with the inference feature of fuzzy logic. The ANFIS system, which is used in the solution of many problems such as classification and estimation of deep learning applications, meets the needs in many different areas such as modeling, control, and parameter estimation. In recent years, heuristic methods have been used for the training of this network, which requires initial and result parameters by its structure. Moth-Flame Optimization Algorithm (MFO) is one of the current heuristic methods modeled by the influence of the spiral movement of the moths towards the light source. In this study, the MFO algorithm was used for the first time for the optimization of initial and result parameters in the ANFIS system. In the determination of parameters, nonlinear system identification, time series estimation, classification problems were tried to be solved. When the results obtained for the ANFIS trained with the known heuristic methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA) and the results of ANFIS trained by the MFO were examined, it was observed that the MFO had lower error values.

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Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification

Cited by 30 publications

References 13 publications

Prediction Of Lamp Price Using Adaptive Neuro Fuzzy Inference System

Prediction Of Lamp Price Using Adaptive Neuro Fuzzy Inference System

A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Training Anfis System with Moth-Flame Optimization Algorithm

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