Neurofuzzy systems have been developed as grey box modelling technique ideal for the task of system identiication. Neurofuzzy models combine the mathematical structure of Associative Memory Networks (AMNs) with the transparency of fuzzy systems. This produces a modelling technique to which mathematical analysis can be applied, while being more transparent than traditional black box models. Unfo...

A self-tuning neurofuzzy integrating controller is derived in this paper for offset eliminating purpose. CARIMA plant model is used and the control law produces integral control terms in a natural way. Neurofuzzy networks are chosen to implement the direct self-tuning nonlinear integrating controller. The performance of the self-tuning integrating neurofuzzy controller is illustrated by example...

An online fault detection and isolation scheme for nonlinear systems based on neurofuzzy modelling and pattern matching is developed in this paper. The system is first modelled offline by a neurofuzzy network using data obtained under normal operating conditions. Another neurofuzzy network is then used to model the residual, which is the difference between the output of the system and that from...

Understanding data is usually done extracting fuzzy or crisp logical rules using neurofuzzy systems, decision trees and other approaches. Prototypebased rules are an interesting alternative providing in many cases simpler, more accurate and more comprehensible description of the data. Algorithm for generation of threshold prototype-based rules are described and a comparison with neurofuzzy syst...

The paper focuses on the problem of robust fault detection using neurofuzzy model based strategies. The main objective of the work is to show how to employ bounding error approach to determine the uncertainty of the neurofuzzy model and next utilize this knowledge for robust fault detection. The paper presents also how to tackle the problem of choosing the right structure of the neurofuzzy mode...

Model-reference adaptive control with neurofuzzy methodology is derived in this paper. Associate memory network(AMN) is investigated in detail to be the possible implementation as the direct self-tuning nonlinear controller. The essence of the neurofuzzy controller has been discussed and the local stability of the system is reached. The performance of the model-reference adaptive neurofuzzy con...

This paper addresses the problem of rate control for Available Bit Rate (ABR) service class in Asynchronous Transfer Mode (ATM) networks. An adaptive neurofuzzy mechanism based on Adaptive Network Fuzzy Inference System (ANFIS) for allocating rates in ABR service has been proposed and compared with the fuzzy technique called as Fuzzy Explicit Rate Marking (FERM). To achieve this, a neurofuzzy A...

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 ...

In this work the application of a neurofuzzy control system in a multimachine power system is presented. The neurofuzzy control is obtained by the off-line training of a fuzzy neural network based on

Owing to the evolution of the smart grid, the emergence of hybrid power systems (HPSs), and the proliferation of plug-in-hybrid electric vehicles, the development of efficient and robust maximum power point tracking (MPPT) algorithms for renewable energy sources due to their inherent intermittent nature has overwhelmed the power industry. In this paper, an incremental conductance (IC) based Her...