Evolving Fuzzy Systems

Angelov, Plamen

doi:10.1007/978-0-387-30440-3_192

Cited by 9 publications

(9 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The evolving systems are viewed basically in the context of data streams, as it is especially use ful to addr ess ' shift' and ' drift' concepts [ 11], [ 12], describing the changes of statistical characteristics of the data stream in time. Basically, the term has arisen in application to the fuzzy systems frameworks [ 13], but it can be applied to neur al networks, particularly evolving spiking neural networks [ 14], SVM models [ 15] and other systems. Hence apart of the previous algorithms, the evolving algorithms allow to follow the changes within the pattern by the model adaptation.…”

Section: Introductionmentioning

confidence: 99%

Evolving clustering, classification and regression with TEDA

Kangin

Angelov

2015

2015 International Joint Conference on Neural Networks (IJCNN)

Self Cite

View full text Add to dashboard Cite

In th i s art i cle the novel cluster i ng and regress i on methods TEDACluster and TEDAPredict methods are descr i bed additionally to recently proposed evolv i ng class i fier TEDAClass. The algor i thms for class i fication, cluster i ng and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive process i ng of large amounts of data. The framework i s capable of computationally cheap exact update of data per sample, and can be used for training 'from scratch'. All three algor i thms are evolv i ng that is they are capable of chang i ng i ts own structure dur i ng the update stage, wh i ch allows to follow the changes with i n the model pattern.

show abstract

Section: Introductionmentioning

confidence: 99%

Evolving clustering, classification and regression with TEDA

Kangin

Angelov

2015

2015 International Joint Conference on Neural Networks (IJCNN)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, GFS, NFS, and HFS system that embraces and adapt itself to the dynamic nature of data is evolving fuzzy systems (EFS). EFS systems embed provisions for dynamic (online) training of systems for streaming (realtime) data Angelov (2009), Kasabov (1998). In EFS, a system incrementally evolves fuzzy rules incoming new data (Lughofer, 2011).…”

Section: Evolving Fuzzy Systemsmentioning

confidence: 99%

Heuristic design of fuzzy inference systems: A review of three decades of research

Ojha

Abraham

Snåšel

2019

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multiobjective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi-Sugano-Kang-type fuzzy systems; whereas, the NFS combines the FIS with neural network learning method to improve the approximation ability. HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. EFS solves the data streaming issues by refining (evolving) the system incrementally, and MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. The overall synthesis of these dimensions explores the FIS's potential challenges and opportunities; the complex relations among the dimension; and the FIS's potential to combining one or more computational frameworks adding another dimension: deep fuzzy systems.

show abstract

“…To overcome these weaknesses, various evolving methods have been developed (Pedram et al 2006;Angelov et al 2006;Angelov andFilev 2004, 2005;Bernard and Lázsló 1981;Deng and Kasabov 2000;Emami et al 1998;Kadirkamanathan and Niranjan 1993;Kasabov 1998Kasabov , 2002Kim et al 1997;Leng et al 2004Leng et al , 2005Lin and Shen 2006;Lu et al 1997;Mousavi et al 2007;Nelles et al 2000;Pedrycz 2006;Platt 1991;Qin and Yang 2007;Rung et al 2006;Sugeno and Yasukawa 1993;Takagi and Sugeno 1985;Tsekouras 2005;Wang et al 2010;Wang and Yen 1999;Wu and Er 2000;Wu et al 2001). These methods could be classified into two main approaches.…”

Section: Introductionmentioning

confidence: 98%

A structure evolving learning method for fuzzy systems

2010

View full text Add to dashboard Cite

This paper proposes a structure evolving learning method for fuzzy systems. The mechanism of the algorithm is that it is an error-reducing driven learning method. The proposed algorithm starts with a simple fuzzy system and evolves the system structure by adding more fuzzy terms and rules to reduce the model errors in a 'greedy' way. The main features of the proposed algorithm are summarized as three points. Firstly, it can automatically determine and control the number and location of fuzzy terms by following the error-reducing driven evolving process to achieve the desired accuracy. Secondly, it adds new fuzzy terms and rules by evenly distributing error to each sub-region aiming at an efficient set of fuzzy rules. Thirdly, it uses triangular membership functions with regular partitions and leads to fuzzy system models with good transparency and interpretability. Three benchmark examples are given to illustrate the advantages of the proposed algorithm.

show abstract

Evolving Fuzzy Systems

Cited by 9 publications

References 60 publications

Evolving clustering, classification and regression with TEDA

Evolving clustering, classification and regression with TEDA

Heuristic design of fuzzy inference systems: A review of three decades of research

A structure evolving learning method for fuzzy systems

Contact Info

Product

Resources

About