In this paper we present a complete design methodology to obtain a fuzzy model with an
Adaptive Neuro Fuzzy Inference System (ANFIS). This methodology consists of three …

This paper introduces a new learning algorithm for artificial neural networks, based on a
fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with …

In this paper we proposed a fuzzy neural network model which can embody a fuzzy Takagi-
Sugeno model and carry out fuzzy inference and support structure of fuzzy rules. The …

In this paper we present a new method to generate interpretable fuzzy systems from training
data. A fuzzy system is developed for nonlinear systems modeling and for system state …

The accurate modeling of a time series using a Sugeno Fuzzy Inference System (FIS)
requires an algorithm that can train the FIS to minimize the error of seen and unseen data …

We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using
adaptive neuro-fuzzy inference systems (ANFIS). The method is tested on two real-world …

Neural networks and fuzzy inference systems have been widely used in several intelligent
multimedia applications. Artificial Neural Network (ANN) learns from scratch by adjusting the …

Nonlinear dynamic time series modeling is a generic problem, which permeates all fields of
science. The authors have developed a soft computing based methodology for the modeling …

The paper briefly presents and compares four neuro-fuzzy systems used for rule-based
modelling of dynamic processes (chaotic Mackey-Glass time series). The following systems …

A growing problem in town areas is noise pollution due to the increasing number of vehicles
that daily cross cities. A classical approach to model this kind of system is based on …