An on-line algorithm for creating self-organizing fuzzy neural networks

Leng, Gang; Prasad, Girijesh; McGinnity, T.M.

doi:10.1016/j.neunet.2004.07.009

Cited by 160 publications

(120 citation statements)

References 27 publications

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“…Nowadays, constructive methods for flexible modeling and identification have attracted the atention 1,19,32,38 . Several authors have extended the neurofuzzy models in order to endow them with some constructive capabilities.…”

Section: The State Of the Art Of Constructive Methodsmentioning

confidence: 99%

“…This makes it hard to estimate the overall mapping directly from the consequent of each rule output. Finally, a self-organizing partitioning solves the input space partitioning problem by means of a learning algorithm which automates structure and parameters identification simultaneously 1,19,32,38 . This type of partitioning has a strong dependence on the selforganizing operations.…”

Section: Introductionmentioning

confidence: 99%

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SONFIS: Structure Identification and Modeling with a Self-Organizing Neuro-Fuzzy Inference System

Allende‐Cid¹,

Salas²,

Veloz³

et al. 2016

IJCIS

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This paper presents a new adaptive learning algorithm to automatically design a neural fuzzy model. This constructive learning algorithm attempts to identify the structure of the model based on an architectural self-organization mechanism with a data-driven approach. The proposed training algorithm self-organizes the model with intuitive adding, merging and splitting operations. Sub-networks compete to learn specific training patterns and, to accomplish this task, the algorithm can either add new neurons, merge correlated ones or split existing ones with unsatisfactory performance. The proposed algorithm does not use a clustering method to partition the input-space like most of the state of the art algorithms. The proposed approach has been tested on well-known synthetic and real-world benchmark datasets. The experimental results show that our proposal is able to find the most suitable architecture with better results compared with those obtained with other methods from the literature.

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Section: The State Of the Art Of Constructive Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SONFIS: Structure Identification and Modeling with a Self-Organizing Neuro-Fuzzy Inference System

Allende‐Cid¹,

Salas²,

Veloz³

et al. 2016

IJCIS

View full text Add to dashboard Cite

show abstract

“…Thus the expected firing strength of rule i would be the average of LFS i and UFS i . The significance of the proposed simple approach is apparent for its low computational overhead and run-time performance over comparable algorithms [15][16][17], [53], [54] for real-time classification of brain signals. The type-2 classifier rule and inference generation using the above rule is represented in the form of a type-2 fuzzy neuron (Fig.…”

Section: Classifier Selection and Designmentioning

confidence: 99%

“…Table VIII includes the result of mean percentage classification accuracies of type-2 fuzzy classifiers against traditional ones, including self-organized fuzzy neural network (SOFNN) [53], artificial neural network fuzzy inference system (ANFIS) [54] and three existing IT2FS-induced models [15]- [17]. The experiment was performed on 10 subjects, each participating in 10 sessions, comprising 9 stimuli, covering 10 ×10 ×9= 900 traffic instances.…”

Section: B Performance Analysis Of the Type-2 Mpfd Classifiermentioning

confidence: 99%

“…Details of the subjective performance of the type-2 classifiers using the aforesaid metrics without and with phone calls are given in [47]. [15] 96.34 milliseconds IT2FS-NN (Lee et al) [16] 98.26 milliseconds IT2FS-NN (Lin et al) [17] 92.42 milliseconds SVM 38.25 milliseconds ANFIS [54] 100.02 milliseconds SOFNN [53] 112.04 milliseconds GT2FS-NN (proposed) 96.02 milliseconds Type-1 Fuzzy NN 50.4 milliseconds [47]). …”

Section: B Performance Analysis Of the Type-2 Mpfd Classifiermentioning

confidence: 99%

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EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

Saha

Konar

Nagar

2017

IEEE Trans. Emerg. Top. Comput. Intell.

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Evolving Systems: Review

Angelov

Iglesias

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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In this article, we describe the research area of evolving systems starting with a brief history, the basic concepts, and definitions and moving to classes of evolving systems. We further provide illustrative examples of evolving systems to various problems. The research area of evolving systems is closely related to adaptive systems, machine learning, mathematical modeling, data science, and computer systems. Some systems with such properties emerged at the end of last century, but the area was formed and recognized in the current century. The importance of this research – which is now a recognized niche topic with its own scientific journal (Evolving Systems), annualIEEEconferences, technical committees, etc. – is even clearer now when the era of Big Data, data streams, machine learning, and intelligence is a topic not only of research but also of publicity. The importance of evolving systems was stressed at the very beginning of its formation as a subdiscipline because a true intelligence can only be evolving. Indeed, the majority of the real processes and phenomena that people are interested in, such as modeling, predicting, classifying, controlling, or simply monitoring, are nonstationary; they are complex, nonlinear, and dynamically evolving. This includes climate, human behavior, social and biomedical systems, and even contemporary technical systems. The reality in which we are living now, couple of decades after the appearance of evolving systems, is characterized not only by a huge volume of exponentially growing data but also by their dynamically evolving nature, heterogeneous form, uncertainty, and often lack of structure. The classical theories and practical toolset (techniques, algorithms, and methods) have their own limitations and often rely on unrealistic assumptions. The concept of evolving systems was revolutionary and broke a number of these assumptions bringing the results closer to the reality of the problems, processes, and phenomena we study. One such key restrictive assumption usually made is about the fixed structure of the model of the system that describes processes or phenomena we are interested in. This is closely linked with the millennia old principle of approaching complex issues – “divide et Impera.” Adding to it “evolve” is vital.

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An on-line algorithm for creating self-organizing fuzzy neural networks

Cited by 160 publications

References 27 publications

SONFIS: Structure Identification and Modeling with a Self-Organizing Neuro-Fuzzy Inference System

SONFIS: Structure Identification and Modeling with a Self-Organizing Neuro-Fuzzy Inference System

EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

Evolving Systems: Review

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