scholarly journals An Improved ZMP-Based CPG Model of Bipedal Robot Walking Searched by SaDE

ISRN Robotics ◽  
2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
H. F. Yu ◽  
E. H. K. Fung ◽  
X. J. Jing
Keyword(s):  
Fuzzy Inference ◽  
Dynamic Balance ◽  
Kinematic Model ◽  
Angular Position ◽  
Step Length ◽  
Superior Performance ◽  
Bipedal Robot ◽  
Inference System ◽  
Walking Gait ◽  
Differential Evolutionary Algorithm

This paper proposed a method to improve the walking behavior of bipedal robot with adjustable step length. Objectives of this paper are threefold. (1) Genetic Algorithm Optimized Fourier Series Formulation (GAOFSF) is modified to improve its performance. (2) Self-adaptive Differential Evolutionary Algorithm (SaDE) is applied to search feasible walking gait. (3) An efficient method is proposed for adjusting step length based on the modified central pattern generator (CPG) model. The GAOFSF is modified to ensure that trajectories generated are continuous in angular position, velocity, and acceleration. After formulation of the modified CPG model, SaDE is chosen to optimize walking gait (CPG model) due to its superior performance. Through simulation results, dynamic balance of the robot with modified CPG model is better than the original one. In this paper, four adjustable factors (Rhs,support, Rhs,swing, Rks,support, and Rks,swing) are added to the joint trajectories. Through adjusting these four factors, joint trajectories are changed and hence the step length achieved by the robot. Finally, the relationship between (1) the desired step length and (2) an appropriate set of Rhs,support, Rhs,swing, Rks,support, and Rks,swing searched by SaDE is learnt by Fuzzy Inference System (FIS). Desired joint angles can be found without the aid of inverse kinematic model.

Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices

2016 ◽  
Vol 26 (02) ◽  
pp. 1750034 ◽  
Author(s):  
J. Sangeetha ◽  
P. Renuga
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Test System ◽  
Operating Conditions ◽  
Superior Performance ◽  
Control Technique ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Oscillation Damping

This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

scholarly journals PREDICTION OF THE PERFORMANCE OF BITUMINOUS MIXES USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS

2020 ◽  
Vol 5 (6) ◽  
Author(s):  
Jonata Jefferson Andrade ◽  
Leonardo Goliatt Da Fonseca ◽  
Michèle Farage ◽  
Geraldo Luciano de Oliveira Marques
Keyword(s):  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Resilient Modulus ◽  
Critical Issue ◽  
Superior Performance ◽  
Inference System ◽  
Forecast Performance ◽  
Experimental Database ◽  
Neuro Fuzzy

Accurately forecast performance and durability is a critical issue for improving the design of new and existing pavements. The poor pavement performance increases traffic congestion, compromises safety, and raises maintenance costs due to frequent repairs. The resilient modulus is one of the most critical unbound material property inputs in several current pavement design procedures. Recent studies have addressed the problem of resilient modulus prediction using different methods, including computational intelligence approaches. In this paper, a hybrid intelligent system called ANFIS (Adaptive Neuro-Fuzzy Inference System) is used for predicting the resilient modulus from an experimental database of 270 distinct compositions. ANFIS achieved superior performance when estimating the resilient modulus of bituminous mixes, which can potentially save laboratory resources.

scholarly journals Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method

2019 ◽  
Vol 13 (1) ◽  
pp. 293
Author(s):  
Mujiarto Mujiarto ◽  
Asari Djohar ◽  
Mumu Komaro ◽  
Mohamad Afendee Mohamed ◽  
Darmawan Setia Rahayu ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Color Detection ◽  
Anfis Method ◽  
Arduino Microcontroller

<p>In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.</p>

Using Fuzzy Logic to Control Combined Cycle Gas Turbine During Ambient Computing Environment

2020 ◽  
Vol 11 (3) ◽  
pp. 106-130 ◽  
Author(s):  
Mostafa A. Elhosseini
Keyword(s):  
Fuzzy Logic ◽  
Gas Turbine ◽  
Fuzzy Inference ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Superior Performance ◽  
Rule Base ◽  
Computing Environment ◽  
Inference System ◽  
Ambient Computing

The main aim of this article is to analyse and control a combined cycle gas turbine (CCGT) under normal and perturbation loading using a Fuzzy Logic Control (FLC) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) through an ambient computing environment. The main characteristics of ambient computing is invisible, embedded, easy to use, and adaptive to name a few. The current article proposes the employment of FLC and to control the operation of CCGT considering the system inputs uncertainty. The target of the FLC is to maintain the system speed, exhaust temperature, and airflow within the desired interval. ANFIS helps to get the optimal control parameter and construct the proper rule base with an appropriate membership function with reasonable accuracy. The simulation results demonstrate the ANFIS controller's superior performance over FLC as well as the traditional controller for normal operating conditions and load perturbation.

Short- and long-term predictions of novel corona virus using mathematical modeling and artificial intelligence methods

2021 ◽  
pp. 2150028
Author(s):  
Hend I. Alkhammash ◽  
Sattam Al Otaibi ◽  
Nasim ULLAH
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Fuzzy Inference ◽  
Compartmental Models ◽  
Superior Performance ◽  
Kingdom Of Saudi Arabia ◽  
Inference System ◽  
Corona Virus ◽  
Short And Long Term

This paper proposes spread prediction of novel corona virus outbreak using different compartmental models and artificial intelligence (AI) methods. Real data for several months is collected from the Ministry of Health (MOH) website, Kingdom of Saudi Arabia and two compartmental models, namely SIR (susceptible, infectious, recovered) and SEIRD (susceptible, exposed, infectious, recovered, dead) are utilized to best fit the data. AI methods are well suited for short- and long-term stochastic forecasts. Keeping in view the inherent advantages of AI methods, adaptive neuro-fuzzy inference system (ANFIS) models are trained using the collected data to replicate the dynamic behavior of the COVID-19 spread in Kingdom of Saudi Arabia. The prediction comparison for COVID-19 spread is made between the compartmental and ANFIS models for both short- and long-term forecasts of the experimental data. From the presented results, ANFIS-based models show superior performance as compared to compartmental models.

scholarly journals Granular computing–neural network model for prediction of longitudinal dispersion coefficients in rivers

2019 ◽  
Vol 80 (10) ◽  
pp. 1880-1892 ◽  
Author(s):  
Behzad Ghiasi ◽  
Hossein Sheikhian ◽  
Amin Zeynolabedin ◽  
Mohammad Hossein Niksokhan
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Granular Computing ◽  
Fuzzy Inference ◽  
Longitudinal Dispersion ◽  
Superior Performance ◽  
Ann Model ◽  
Longitudinal Dispersion Coefficient ◽  
Inference System ◽  
Advection Dispersion

Abstract Successful application of one-dimensional advection–dispersion models in rivers depends on the accuracy of the longitudinal dispersion coefficient (LDC). In this regards, this study aims to introduce an appropriate approach to estimate LDC in natural rivers that is based on a hybrid method of granular computing (GRC) and an artificial neural network (ANN) model (GRC-ANN). Also, adaptive neuro-fuzzy inference system (ANFIS) and ANN models were developed to investigate the accuracy of three credible artificial intelligence (AI) models and the performance of these models in different LDC values. By comparing with empirical models developed in other studies, the results revealed the superior performance of GRC-ANN for LDC estimation. The sensitivity analysis of the three intelligent models developed in this study was done to determine the sensitivity of each model to its input parameters, especially the most important ones. The sensitivity analysis results showed that the W/H parameter (W: channel width; H: flow depth) has the most significant impact on the output of all three models in this research.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

Sign in / Sign up

Export Citation Format

Share Document