This paper first presents a discussion of the reasoning and method for combining neural networks and fuzzy logic. The problem of moving a robotic arm in the presence of an obstacle is discussed. Several neuro-fuzzy controllers are trained using sample data obtained from a human’s control of a robotic arm. Their performance is quantified and compared. It is shown that the definition of the fuzzy...

Fuzzy technology is adaptive and easily applicable in different areas.Fuzzy logic provides powerful tools to capture the perception of natural phenomena. The paper deals with tuning of neuro-fuzzy controllers for integrating plant and for integrating plants with time delay. The designed approach is verified on three examples by simulations and compared plants with classical PID control. Designe...

This paper proposes a Single Input Neuro-fuzzy Logic Controller based on Radial Basis Function Network (SI-NFRBFN) for non-linear systems. To obtain single input from multi inputs a Distance Method is suggested. Using this method all the uncertain inputs are simplified into a single input known as distance. With the help of this variable the control unit matrix introduced in Hybrid Neuro-fuzzy ...

Neuro-fuzzy approaches are developed, in the present work, to determine time-optimal, collision-free path of a car-like mobile robot navigating in a dynamic environment. A fuzzy logic controller (FLC) is used to control the robot and the performance of the FLC is improved by using three different neuro-fuzzy (NN-FLC) approaches. The performances of these neuro-fuzzy approaches are compared amon...

Soft computing is an association of computing methodologies that includes fuzzy logic, neuro-computing, evolutionary computing, and probabilistic computing. After a brief overview of Soft Computing components, we will analyze some of its most synergistic combinations. We will emphasize the development of smart algorithm-controllers, such as the use of fuzzy logic to control the parameters of ev...

This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy controller shows better results in comparison to triangular fuzzy controller. Secondly, ...

A modified nuero fuzzy based conditional shortest path routing protocol for wireless mesh network is simulated and studied. In wireless mesh networks many routing protocols used for conditional shortest path routing like AODV, by considering only the shortest route to destination .The data transfer in wireless mesh networks is to and from the AP. These protocol congested the routes and overload...

Industrial heating furnace has a great deal of special characteristics such as big capacity, long lag and non-linear trait, etc. In order to control it better, we present a sort of fuzzy neural network temperature control model. It can transform the rulers of fuzzy logic control to a pair of input-output samples of multilayer forward neural network.The knowledge is not expressed by a serial of ...

This paper introduced modeling and simulation results of an isolated solar photovoltaic system with lead-acid battery. The is operated at maximum power point tracking (MPPT) using MATLAB/Simulink environment. proposed controllers used in this are proportional – integral (PI) controller, fuzzy logic controller (FLC) Adaptive Neuro Fuzzy Inference System (ANFIS) controllers. for controlling dc-dc...

We examine various and different approaches for the prediction of economic crisis periods of US economy. We examine the traditional econometric discrete choice Logit and Probit models then a feed-forward neural network (FFNN) model and finally we apply an Adaptive Neuro-Fuzzy Inference System (ANFIS). We examine the period 1950-2009, where we take as the in-sample or training period 1950-2005, ...