Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Backpropagation gradient descent method was performed to train the ANFIS system. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in controlling the non linear system
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system ...
Nonlinear system identification is becoming an important tool which can be used to improve control p...
This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation proble...
A new speed control approach based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to a closed-...
Abstract — This paper, discusses about navigation control of mobile robot using adaptive neuro-fuzzy...
The application of supervised learning to train an intelligent vehicle with a neuro-fuzzy controller...
ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capa...
A hybrid intelligent control technique based on combination of neural network and fuzzy logic will b...
Neuro-fuzzy controller to navigate an unmanned vehicle Boumediene Selma * and Samira Chouraqui* A Ne...
Adaptive Neural Fuzzy Inference Systems ANFIS have an increasing tendency to be used in scientific r...
ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capa...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system ...
Nonlinear system identification is becoming an important tool which can be used to improve control p...
This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation proble...
A new speed control approach based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to a closed-...
Abstract — This paper, discusses about navigation control of mobile robot using adaptive neuro-fuzzy...
The application of supervised learning to train an intelligent vehicle with a neuro-fuzzy controller...
ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capa...
A hybrid intelligent control technique based on combination of neural network and fuzzy logic will b...
Neuro-fuzzy controller to navigate an unmanned vehicle Boumediene Selma * and Samira Chouraqui* A Ne...
Adaptive Neural Fuzzy Inference Systems ANFIS have an increasing tendency to be used in scientific r...
ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capa...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathe...
The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system ...