Application of a Hybrid Neural Fuzzy Inference System to Forecast Solar Intensity

In this paper, we propose a control strategy for a nonholonomic robot which is based on an Adaptive Neural Fuzzy Inference System. The neuro-controller makes it possible the robot track a desired reference trajectory. After a short reminder about Adaptive Neural Fuzzy Inference System, we describe the control strategy which is used on our virtual nonholonomic robot. And finally, we give the simulations’ results where the robot have to pass into a narrow path as well as the first validation results concerning the implementation of the proposed concepts on real robot.

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Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v4i2.pp109-123 ◽

2015 ◽

Vol 4 (2) ◽

pp. 109

Author(s):

Panchand Jha

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Inference System ◽

Inverse Kinematics ◽

Fuzzy Inference ◽

Robot Manipulator ◽

Ann Model ◽

Inference System ◽

Artificial Neural ◽

Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

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Numerical study on the feasibility of dynamic evolving neural-fuzzy inference system for approximation of compressive strength of dry-cast concrete

Applied Soft Computing ◽

10.1016/j.asoc.2014.08.010 ◽

2014 ◽

Vol 24 ◽

pp. 572-584 ◽

Cited By ~ 9

Author(s):

Jafar Sobhani ◽

Meysam Najimi

Keyword(s):

Compressive Strength ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Numerical Study ◽

Inference System ◽

Cast Concrete ◽

Neural Fuzzy

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Modeling and predict environmental indicators for land leveling using adaptive neuro-fuzzy inference system (ANFIS), and regression

International Journal of Energy Sector Management ◽

10.1108/ijesm-02-2017-0003 ◽

2018 ◽

Vol 12 (4) ◽

pp. 484-506 ◽

Cited By ~ 1

Author(s):

Farhad Mirzaei ◽

Mahmoud Delavar ◽

Isham Alzoubi ◽

Babak Nadjar Arrabi

Keyword(s):

Energy Consumption ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Environmental Indicators ◽

Inference System ◽

Content Type ◽

Bee Colony ◽

Fill Volume ◽

Land Leveling ◽

Neural Fuzzy

PurposeThe purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.Design/methodology/approachThis paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).FindingsBy applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highestR2value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highestR2with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.Originality/valueAs land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.

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