For double inverted pendulum multivariable, strong coupling and nonlinear proposed adaptive fuzzy neural inference system (ANFIS) is applied inverted pendulum stabilization control process. Adaptive control algorithm, fully able to meet the requirements of double inverted pendulum control, ANFIS system after training, will be applied to the inverted pendulum system controller has better control...

The most common technique for the parallelization of multigrid methods is grid partitioning. For such methods Brandt and Diskin have suggested the use of a variant of segmental reenement in order to reduce the amount of inter{processor communication. A parallel multigrid method with this technique avoids all communication on the nest grid levels. This article will examine some features of this ...

We present a new algorithm for the generation of 3-dimensional (3-D) grids for the simulation of semiconductor devices. The fitting of the device geometry and the required mesh density is obtained by partitioning the elements at an optimal point at each refinement step. This allows the fitting of more general 3-D device geometries and the reduction of grid points in comparison with previous gri...

In this paper we presented an architecture and basic learning process underlying in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of adaptive network. In real world computing environment, soft computing techniques including neural network, fuzzy logic algorithms have been widely used to derive an actual decision using given i...

This paper discusses partitioning of dynamic structured grid hierarchies, occuring in structured adaptive mesh re nement (SAMR) applications. When a SAMR method is executed on a parallel computer, the work load will change dynamically. Thus, there is need for dynamic load balancing. Inverse spacelling curve partitioning (ISP) is appealing for load balancing in parallel SAMR, because of its spee...

In this paper, an attempt has been made to design an computational intelligence technique based expert system using Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting surface roughness in end milling of Inconel 718. Two different types of membership functions are adopted for analysis in ANFIS training and compared their differences regarding the accuracy rate of the surface roughness ...

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. Back-propagation gradient descent method was performed to train the ANFIS system. The performance of the...

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed th...

The application of neuro-fuzzy inference system to predict the compressive strengths of concrete is presented in this study. To investigate the influence of various parameters which affect the compressive strength, 2000 data samples were used for the analysis. Adaptive neuro-fuzzy inference system (ANFIS) was introduced for training and testing the data obtained from technical literatures. To r...

Supplier selection is a key task for firms, enabling them to achieve the objectives of a supply chain. Selecting a supplier is based on multiple conflicting factors, such as quality and cost, which are represented by a multi-criteria description of the problem. In this article, a new approach based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented to overcome the supplier selection ...