in this paper, an adaptive neuro fuzzy inference system (anfis) based control is proposed for the tracking of a micro-electro mechanical systems (mems) gyroscope sensor. the anfis is used to train parameters of the controller for tracking a desired trajectory. numerical simulations for a mems gyroscope are looked into to check the effectiveness of the anfis control scheme. it proves that the sy...

Search engines are crucial for information gathering systems (IGS). New challenges face search concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the process. Based on Multilayer Fuzzy Inference System (MFIS), first step implement scalable relay logical rules in order produce three classifications behavior, profiles, and query...

the present article investigates the application of high order tsk (takagi sugeno kang) fuzzy systems in modeling photo voltaic (pv) cell characteristics. a method has been introduced for training second order tsk fuzzy systems using anfis (artificial neural fuzzy inference system) training method. it is clear that higher order tsk fuzzy systems are more precise approximators while they cover n...

Aneural-learning fuzzy technique is proposed for T–S fuzzy-model identification ofmodel-free physical systems. Further, an algorithm with a defined modelling index is proposed to integrate and to guarantee that the proposed neural-based optimal fuzzy controller can stabilize physical systems; the modelling index is defined to denote the modelling-error evolution, and to ensure that the training...

This paper presents a novel hybridization of the fuzzy logic, the neural network and the coevolutionary algorithm for building a fuzzy-neural system (or a Mamdani fuzzy system) from data. The novel hybridization uses the coevolution of many species, and proposes the coevolution of groups of similar species, both for the optimization of the structure of the fuzzy-neural network. In the fuzzy-neu...

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a ‘black box’. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classifica...

suitable soil structure is important for crop growth. one of the main characteristics of soil structure is the size of soil aggregates. there are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most common method. in this paper, a method based on adaptive neuro fuzzy inference system (anfis) was used...

compensatory genetic fuzzy neural networks and their applications neural networks fuzzy logic and genetic algorithms by rajasekaran and g a v pai ebook free download nonlinear workbook chaos fractals cellular automata neural networks genetic algorithms gene expression programming wavelets fuzzy logic with c java and symbolicc programs applications of neural networks in environment energy and he...

This paper proposes an adaptive TSK-type fuzzy network control (ATFNC) system for synchronization of a coupled nonlinear chaotic system. The design of the proposed ATFNC system is comprised of a neural controller and a fuzzy compensator. The neural controller uses a Takagi-Sugeno-Kang (TSK)-type fuzzy neural network (TFNN) to online mimic an ideal controller and the fuzzy compensator is designe...

The paper presents a short review how to use feedforward neural networks for non-linear system identification, with application at the neural implementation of a fuzzy system. In this application the inputoutput transfer characteristics of the fuzzy system are used to evaluate the accuracy of the identification results expressed for a neuro-fuzzy model. This method could be used for identificat...