Intelligent autonomous robots and multiagent systems, having different skills and capabilities for specific subtasks, have the potential to solve problems more efficiently and effectively. In this paper both f i m y logic (FL) and subtractive clustering (SC) are used for the design of autonomous robot behaviours. The design procedure is conducted in two stages: first subtractive clustering is a...

In this paper, the discrete-time nonlinear systems identification and control based on an adaptive filter are introduced. This adaptive filter is mplemented using the adaptive network called Multi Input Fuzzy Rules Emulated Network (MIFREN). Inspired by the neuro-fuzzy network, the tructure of MIFREN resembles the human knowledge in the form of fuzzy IF-THEN rules. The initial value of MIFREN’s...

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...

It is known that neuro-fuzzy system is easily stuck in local minimum. To improve these drawbacks, a two-stage algorithm combining the advantages of neuro-fuzzy and genetic algorithms (GA) is integrated in system identification. Genetic algorithms are general purposed optimization algorithms with adaptive reproduction, crossover, and mutation operators that provide a method to search optimal par...

The survey and modeling of the deformations of large structures is a major task in engineering geodesy. In this paper, a new procedure to describe and predict the deformations is presented and discussed which is based on Neuro-Fuzzy modeling. Neuro-Fuzzy methods are data driven; they deduce the model directly from the data. Hence, they are mostly convenient if there are no physical models avail...

Brain tumor image classification and segmentation are an important but inherently difficult problem in magnetic resonance (MR) medical images. Artificial neural networks employed for image classification problems do not guarantee high accuracy besides being computationally heavy. The necessity for a large training set to achieve high accuracy is another drawback of ANN. On the other hand, fuzzy...

A new methodology for simulating the flow field inside an arteriovenous (AV) graft to vein anastomoses by the adaptive neuro fuzzy inference system (ANFIS) is presented in this study. For determining the optimal AV graft angle, an ANFIS-based model of neuro fuzzy-graft-vein (NF-GVEIN) is proposed. Therefore engineering design of the graft can be supported. The advantage of this neuro-fuzzy hybr...

We designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three ‘swing phase objectives’, characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller and a neural network, which makes the controller self tuning and adaptive. Two experiments have been perf...

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the tested on NASDAQ stock market indices which among most widely followed United States. Empirical results show that by determining parameters ANFIS (premise and consequent parameters) using GA, we can improve performance terms Mean...

This paper presents a neuro-fuzzy system to speech classification. We propose a multi-resolution feature extraction technique to deal with adaptive frame size. We utilize fuzzy adaptive resonance theory (FART) to cluster each frame. FART was an extension to ART, performs clustering of its inputs via unsupervised learning. ART describes a family of self-organizing neural networks, capable of clu...