This paper combines adaptive network fuzzy inference system (ANFIS) and finite element method (FEM) to study the die shape optimal design in sheet metal bending process. At first, the explicit dynamic FEM is used to simulate the sheet metal bending process. After the bending process, the springback is analyzed by using the implicit static FEM to establish the basic database for ANFIS. Then, the...

In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive NeuroFuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of MagnetoRheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavior o...

This paper proposes an artificialintelligence-based solution to interface photovoltaic (PV) array with the three phase ac load and to deliver maximum power to the load. The maximum power delivery to the load is achieved by MPPT controller which employs adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS-based MPPT offers an extremely fast dynamic response with great accuracy. The ...

Fault diagnosis of steam turbine is essential to predict further development and to anticipate it by taking appropriate measures. Fault diagnosis of modern industrial power plants by human inspection is time-consuming and expensive as well as fault diagnostic system modelling based on conventional mathematical tools is not suitable for ill defined and uncertain system. Therefore, it is necessar...

This paper investigates the ability of genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) techniques for groundwater depth forecasting. Five different GP and ANFIS models comprising various combinations of water table depth values from two stations, Bondville and Perry, are developed to forecast one-, twoand three-day ahead water table depths. The root mean square errors...

This paper presents a new general purpose neuro-fuzzy controller to realize adaptive-network-based fuzzy inference system (ANFIS) architecture. ANFIS which tunes the fuzzy inference system with a back propagation algorithm based on collection of input-output data makes fuzzy system to learn. To implementing this idea we propose several improved CMOS analog circuits, including Gaussian-like memb...

The main goal of this research is to develop a novel optimum neuro-fuzzy system for diagnosis the complex and dynamic systems. .It has used the Particle Swarm Optimization (PSO) technique for training the Adaptive Neuro Fuzzy Inference System (ANFIS) off-line. The proposed system has applied for diagnosis the faults of two complex Photovoltaic (PV) systems. They are used to feed the power for l...

This paper reviews the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for vector-controlled induction motor drives. While conventional schemes do not deal well with the highly nonlinear nature of motor control, fuzzy logic with its adjustability and neural networks with their adaptability have been shown to be excellent alternatives. ANFIS combines the advantages of fuzzy logic and neura...

Conventional control algorithms used in pH control systems give inefficient performance, leading to use of large mixers. To improve the neutralization control process, an ANFIS based advanced controller has been proposed. In this paper, method of design of adaptive controller based on neurofuzzy technique is presented. The method uses ANFIS methodology to automatically generate fuzzy rule base ...