Fuzzy systems can approximate any continuous nonlinear function to arbitrary accuracy, provided that suitable fuzzy rules are available (Wang 1994). Recent results show that the fusion of some intelligent technologies with fuzzy systems seems to be very effective for nonlinear systems modelling. In Oh, Pedrycz, and Roh (2006), fuzzy neural networks endowed with polynomial neurons were investiga...

The paper presents a study upon the possibility to use adaptive-network-based fuzzy inference method (ANFIS) in the identification of distributed parameter systems, implementing a distributed sensor network in the system. Some main properties of different identification methods are presented with possible application. The fuzzy systems, implemented using rule bases, fuzzy values, membership fun...

One approach for system identification among many others is the fuzzy identification approach. The advantage of this approach compared to other analytical approaches is, that it is not necessary to make an assumption for the model to be used for the identification. In addition, the fuzzy approach can handle nonlinearities easier than analytical approaches. The Fuzzy–ROSA method is a method for ...

Fingerprint identification is one of the salient areas in biometric identification system. The quality of the fingerprint image is imperative for a veracious matching process. Normally, the contrast of the image is improved during the preprocessing phase of fingerprint matching. Contrast refers to the difference between two contiguous pixels. There are several enhancement techniques available f...

Fuzzy rule derivation is often difficult and time-consuming, and requires expert knowledge. This creates a common bottleneck in fuzzy system design. In order to solve this problem, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a fuzzy neural network based on mutual subsethood (MSBFNN) and its fuzzy rule identificatio...

Plant monitoring and diagnosis are usually integrated as one process to detect and isolate suspicious symptoms and use these to identify the root cause of the failure [1, 2]. The research reported here enables a new plant monitoring and diagnosis framework that employs multiple fuzzy rule-based decision-support system at different diagnosing stages. By employing fuzzy sets and by constructing a...

This article presents a novel fuzzy identification method for dynamic modelling of quadrotor unmanned aerial vehicles. The method is based on a special parameterization of the antecedent part of fuzzy systems that results in fuzzy-partitions for antecedents. This antecedent parameter representation method of fuzzy rules ensures upholding of predefined linguistic value ordering and ensures that ...

A two-layer Recurrent Neural Network Model (RNNM) and an improved Backpropagation-through-time method of its learning are described. For a complex nonlinear plants identification, a fuzzy-neural multi-model, is proposed. The proposed fuzzy-neural model, containing two RNNMs is applied for real-time identification of nonlinear mechanical system. The simulation and experimental results confirm th...

Abstract: An adaptive identifier for neuro-fuzzy control system nonlinear dynamic object operating in conditions of uncertainty intrinsic properties and the environment. The algorithms of structural and parametric identification in real time are a combination of an identification algorithm coefficients of linear management and methods of the theory of interactive adaptation. Adaptive neuro-fuzz...

This paper proposes a Self-Evolving Interval Type-2 Fuzzy Neural Network (SEIT2FNN) for nonlinear systems identification. The SEIT2FNN has both on-line structure and parameter learning abilities. The antecedent parts in each fuzzy rule of the SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules are of the Takagi-Sugeno-Kang (TSK) type. An on-line clustering method is proposed to generate...