Predicting mechanical properties of elastomeric modified nylon blend using adaptive neuro-fuzzy interference system and neural network

2014 ◽  
Vol 76 (5-8) ◽  
pp. 961-970
Author(s):  
M. Ghatarband ◽  
Z. A. Asadi ◽  
S. Mazinani ◽  
M. R. Kalaee ◽  
M. E. Shiri
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Neuro Fuzzy

The Design of Ship Course Intelligent Controller Based on Adaptive Neural Fuzzy Interference System

2011 ◽  
Vol 135-136 ◽  
pp. 1037-1043
Author(s):  
Guan Shan Hu ◽  
Hai Rong Xiao
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
High Performance ◽  
Learning Algorithm ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Ship Steering ◽  
Neuro Fuzzy ◽  
Online Learning Algorithm ◽  
Adaptive Control Algorithm

Under the condition that the nonlinearity of ship steering model is considered and the assumption that the parameters of the model are uncertain, we proposed an adaptive control algorithm for ship course nonlinear system by incorporating the technique of neural network and fuzzy logic system. In the paper, we presented the structure and characteristics of Adaptive Neuro-Fuzzy Interference System (ANFIS), established the ship course controller, and realized an online learning algorithm to do online parameter estimation. We utilize fuzzy logic to solve the uncertainty problem of control system, neural network to optimize the controller parameters. To demonstrate the applicability of the proposed method, simulation results are presented at the end of this paper. The experiment shows that the ANFIS controller can achieve high performance control under parameter perturbation and other disturbances.

Application of neural network expert systems for monitoring, diagnostics and forecasting of technical condition of aircraft engines

2020 ◽  
pp. 51-59
Author(s):  
Д.О. Пушкарёв
Keyword(s):  
Neural Network ◽  
Expert Systems ◽  
Gas Turbine ◽  
Technical Condition ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Monitoring And Diagnostics ◽  
Technical Operation

Рассматривается применение нейросетевых экспертных систем в области контроля, диагностики и прогнозирования технического состояния авиационных ГТД на основе нечеткой логики. Показана методика для решения таких задач в области технической эксплуатации авиационной техники совместно с использованием фаззи-интерференсной системы программы MATLAB. Используя статистические данные о работе двигателя формируется экспертная система на основе нейронной сети позволяющая осуществлять контроль и диагностику ГТД, а также прогнозировать дальнейшее техническое состояния анализируемого двигателя. The application of neural network expert systems in the field of monitoring, diagnostics and forecasting of the technical condition of aviation gas turbine engines based on fuzzy logic is considered. The technique for solving such problems in the field of technical operation of aircraft and using the fuzzy-interference system of the MATLAB program is shown. Using statistical data on the operation of the engine, an expert system is based on the fundamental of a neural network that provide monitoring and diagnostics of gas turbine engines, as well as predicting the further technical condition of the analyzed engine.

scholarly journals Estimation of maximum scour depth downstream of an apron under submerged wall jets

2019 ◽  
Vol 21 (4) ◽  
pp. 523-540 ◽  
Author(s):  
Mohammad Aamir ◽  
Zulfequar Ahmad
Keyword(s):  
Scour Depth ◽  
Regression Equations ◽  
Wall Jets ◽  
Statistical Parameters ◽  
Densimetric Froude Number ◽  
Sediment Size ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann

Abstract An analysis of laboratory experimental data pertaining to local scour downstream of a rigid apron developed under wall jets is presented. The existing equations for the prediction of the maximum scour depth under wall jets are applied to the available data to evaluate their performance and bring forth their limitations. A comparison of measured scour depth with that computed by the existing equations shows that most of the existing empirical equations perform poorly. Artificial neural network (ANN)- and adaptive neuro-fuzzy interference system (ANFIS)-based models are developed using the available data, which provide simple and accurate tools for the estimation of the maximum scour depth. The key parameters that affect the maximum scour depth are densimetric Froude number, apron length, tailwater level, and median sediment size. Results obtained from ANN and ANFIS models are compared with those of empirical and regression equations by means of statistical parameters. The performance of ANN (RMSE = 0.052) and ANFIS (RMSE = 0.066) models is more satisfactory than that of empirical and regression equations.

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