A Neoteric Approach to Rough Neuro Fuzzy Methods

2006 ◽  
Author(s):  
S. Chandana ◽  
R.V. Mayorga
Keyword(s):  
Fuzzy Methods ◽  
Neuro Fuzzy

scholarly journals Analisis Tingkat Akurasi Metode Neuro Fuzzy dalam Prediksi Data IPM di NTB

2019 ◽  
Vol 3 (1) ◽  
pp. 58
Author(s):  
Aenul Muhajirah ◽  
Eka Safitri ◽  
Titin Mardiana ◽  
Hartina Hartina ◽  
Andi Setiawan
Keyword(s):  
Artificial Intelligence ◽  
Human Development ◽  
Human Development Index ◽  
Development Index ◽  
Fuzzy Methods ◽  
Neuro Fuzzy ◽  
Forecasting System

Abstrak: Penelitian ini dilakukan untuk menganalisis proses forecasting dalam menentukan tipe terbaik yang digunakan pada sistem peramalan (forecast). Pada penelitian ini data yang digunakan yaitu data IPM Provinsi Nusa Tengara Barat (NTB) tahun 2008-2018 untuk memprediksi data Indeks Pembangunan Manusia (IPM) tahun 2019. Penelitian ini menggunakan metode Artificial Intelligence Neuro Fuzzy yaitu Fuzzy Mamdani dan ANFIS Sugeno yang diterapkan pada Matlab. Adapun tipe yang diuji adalah Trimf, Trapmf, Gbellmf, Gaussmf, Gauss2mf, Sigmf, Dsigmf, Psigmf, dan Primf. Tipe tersebut bertujuan untuk melihat tingkat akurasi berdasarkan hasil error. Hasil peramalan terbaik didapatkan pada tipe Gauss2mf karena menghasilkan prediksi sebesar 69.5 dengan error sebesar 0.95947 dan MAD sebesar 0.530.354, MSE sebesar 1.570035, MAPE sebesar 0.049273. Abstract: This research was conducted to analyze the forecasting process in determining the best type used in the forecasting system. In this study the data used were the data of West Nusa Tenggara Province (HDI) HDI for the years 2008-2018 to predict the 2019 Human Development Index (HDI) data. This study uses Artificial Intelligence Neuro Fuzzy methods namely Fuzzy Mamdani and ANFIS Sugeno applied to Matlab . The types tested were Trimf, Trapmf, Gbellmf, Gaussmf, Gauss2mf, Sigmf, Dsigmf, Psigmf, and Primf. This type aims to see the level of accuracy based on the results of the error. The best forecasting results were obtained on the Gauss2mf type because it produced a prediction of 69.5 with an error of 0.95947 and MAD of 0.530.354, MSE of 1.570035, MAPE of 0.049273.

Evolutionary Control of Helicopter Hovering Based on Genetic Programming

2013 ◽  
pp. 1-19
Author(s):  
Dimitris C. Dracopoulos ◽  
Dimitrios Effraimidis
Keyword(s):  
Reinforcement Learning ◽  
Genetic Programming ◽  
Control Problems ◽  
Complex Control ◽  
Fuzzy Methods ◽  
Neuro Fuzzy ◽  
Complex Control System ◽  
And Control ◽  
Intelligent Controllers ◽  
Hovering Control

Computational intelligence techniques such as neural networks, fuzzy logic, and hybrid neuroevolutionary and neuro-fuzzy methods have been successfully applied to complex control problems in the last two decades. Genetic programming, a field under the umbrella of evolutionary computation, has not been applied to a sufficiently large number of challenging and difficult control problems, in order to check its viability as a general methodology to such problems. Helicopter hovering control is considered a challenging control problem in the literature and has been included in the set of benchmarks of recent reinforcement learning competitions for deriving new intelligent controllers. This chapter shows how genetic programming can be applied for the derivation of controllers in this nonlinear, high dimensional, complex control system. The evolved controllers are compared with a neuroevolutionary approach that won the first position in the 2008 helicopter hovering reinforcement learning competition. The two approaches perform similarly (and in some cases GP performs better than the winner of the competition), even in the case where unknown wind is added to the dynamic system and control is based on structures evolved previously, that is, the evolved controllers have good generalization capability.

Neuro-Fuzzy Methods for Fault Diagnosis of Nonlinear Systems

2006 ◽  
Vol 6 (9) ◽  
pp. 2020-2030 ◽  
Author(s):  
L. Mehennaoui ◽  
N. Debbache . ◽  
M.L. Benloucif .
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear Systems ◽  
Fuzzy Methods ◽  
Neuro Fuzzy
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