248 research outputs found
A CENTER MANIFOLD THEORY-BASED APPROACH TO THE STABILITY ANALYSIS OF STATE FEEDBACK TAKAGI-SUGENO-KANG FUZZY CONTROL SYSTEMS
The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system
Multiobjective evolutionary optimization of quadratic Takagi-Sugeno fuzzy rules for remote bathymetry estimation
In this work we tackle the problem of bathymetry estimation using: i) a multispectral optical image of the region of interest, and ii) a set of in situ measurements. The idea is to learn the relation that between the reflectances and the depth using a supervised learning approach. In particular, quadratic Takagi-Sugeno fuzzy rules are used to model this relation. The rule base is optimized by means of a multiobjective evolutionary algorithm. To the best of our knowledge this work represents the first use of a quadratic Takagi-Sugeno fuzzy system optimized by a multiobjective evolutionary algorithm with bounded complexity, i.e., able to control the complexity of the consequent part of second-order fuzzy rules. This model has an outstanding modeling power, without inheriting the drawback of complexity due to the use of quadratic functions (which have complexity that scales quadratically with the number of inputs). This opens the way to the use of the proposed approach even for medium/high dimensional problems, like in the case of hyper-spectral images
A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism
© 2015 Elsevier B.V. In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems
Probabilistic clustering algorithms for fuzzy rules decomposition
The fuzzy c-means (FCM) clustering algorithm is the best known and used
method in fuzzy clustering and is generally applied to well defined set of data. In this
paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied
to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one
for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to
the clustering of a flat fuzzy system results a set of decomposed sub-systems that will be
conveniently linked into a Parallel Collaborative Structures
Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces
© 2018 IEEE. Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data
ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS
Artificial intelligence techniques, such as neural
networks and fuzzy logic have shown promising results for
modelling of nonlinear systems whilst traditional approaches are
rather insufficient due to difficulty in modelling of highly
nonlinear components in the system. A laboratory set-up that
resembles the behaviour of a helicopter, namely twin rotor multiinput multi-output system (TRMS) is used as an experimental rig
in this research. An adaptive neuro-fuzzy inference system
(ANFIS) tuned by particle swarm optimization (PSO) algorithm
is developed in search for non-parametric model for the TRMS.
The antecedent parameters of the ANFIS are optimized by a PSO
algorithm and the consequent parameters are updated using
recursive least squares (RLS). The results show that the proposed
technique has better convergence and better performance in
modeling of a nonlinear process. The identified model is justified
and validated in both time domain and frequency domai
Graph Fuzzy System: Concepts, Models and Algorithms
Fuzzy systems (FSs) have enjoyed wide applications in various fields,
including pattern recognition, intelligent control, data mining and
bioinformatics, which is attributed to the strong interpretation and learning
ability. In traditional application scenarios, FSs are mainly applied to model
Euclidean space data and cannot be used to handle graph data of non-Euclidean
structure in nature, such as social networks and traffic route maps. Therefore,
development of FS modeling method that is suitable for graph data and can
retain the advantages of traditional FSs is an important research. To meet this
challenge, a new type of FS for graph data modeling called Graph Fuzzy System
(GFS) is proposed in this paper, where the concepts, modeling framework and
construction algorithms are systematically developed. First, GFS related
concepts, including graph fuzzy rule base, graph fuzzy sets and graph
consequent processing unit (GCPU), are defined. A GFS modeling framework is
then constructed and the antecedents and consequents of the GFS are presented
and analyzed. Finally, a learning framework of GFS is proposed, in which a
kernel K-prototype graph clustering (K2PGC) is proposed to develop the
construction algorithm for the GFS antecedent generation, and then based on
graph neural network (GNNs), consequent parameters learning algorithm is
proposed for GFS. Specifically, three different versions of the GFS
implementation algorithm are developed for comprehensive evaluations with
experiments on various benchmark graph classification datasets. The results
demonstrate that the proposed GFS inherits the advantages of both existing
mainstream GNNs methods and conventional FSs methods while achieving better
performance than the counterparts.Comment: This paper has been submitted to a journa
Using a fuzzy inference system to obtain technological tables for electrical discharge machining processes
Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques
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