Self-Organizing Neuro-Fuzzy Inference System

Allende‐Cid, Héctor; Veloz, Alejandro; Salas, Rodrigo; Chabert, Stéren; Allende, Héctor

doi:10.1007/978-3-540-85920-8_53

Cited by 14 publications

(16 citation statements)

References 5 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 will be measured by means of Mean-Square Error and other performance measures presented in section 5. Some initial results 1 were later improved and made to the original split, merge and grow operators, and a formalization of their corresponding algorithms. Also an exhaustive experimentation was carried out, by validating the model with several real world and synthetic data sets, and meta-heuristics were applied in order to find optimal parameters.…”

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%

See 2 more Smart Citations

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

Allende‐Cid¹,

Salas²,

Veloz³

et al. 2016

IJCIS

Self Cite

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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: The State Of the Art Of Constructive Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Allende‐Cid¹,

Salas²,

Veloz³

et al. 2016

IJCIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this case it is possible to take advantage of the capabilities of other members of the Soft Computing consortium to realize "data driven fuzzy modeling", instead of making a blind choice of operations and prototypical shapes of fuzzy sets. For instance, the learning capability of neural networks can be used to "learn" membership functions of the conditions-fuzzy sets [9], [1], [5], [11], [17] or to learn aggregation connectives [12], to use data driven evolutionary algorithms to optimize the distribution of linguistic terms in a given universe of discourse or even to optimize full fuzzy rules sets [7], to adjust the shape of the transitions between cosupport and core of fuzzy sets [15], or to design and adjust parameterized conjunctions and conditional operations [14], [16].…”

Section: Data Driven Modelingmentioning

confidence: 99%

Reasons for a careful design of fuzzy sets

Trillas¹,

Moraga²

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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This paper is, basically, a reflection on modeling, starting by asking what happens when the meaning of a predicate P is interpreted by the membership function of a fuzzy set that, labeled P , is designed with the information available to the designer on the use of the predicate in the corresponding universe of discourse. In it, the modification of the meaning that P can suffer by its identification with the membership function of the fuzzy set, is analyzed and discussed. It is argued that what can be done for reaching a least possible modification of the meaning, is just a careful design of the membership function. This reflection is further continued at the level of linguistic variables, fuzzy if-then rules, and rule-based systems. It is concluded that as argued in the case of fuzzy sets, the involved operations in rules and systems should be at least carefully chosen, if not specially designed.

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“…However, the locations of the sensors are usually inconsistent with the application requirements. In the papers [11,12] the problem of estimating the field at arbitrary positions of interest, where there are possibly no sensors, from the irregularly placed sensors is considered. The sensor network on a graph is mapped, and by introducing the concepts of interconnection matrices, system digraphs, and cut point sets, real-time field estimation algorithms are derived.…”

Section: Related Workmentioning

confidence: 99%

Efficiency improvement in multi-sensor wireless network based estimation algorithms for distributed parameter systems with application at the heat transfer

Voloşencu

Curiac

2013

EURASIP J. Adv. Signal Process.

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This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods -linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system -to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.

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Self-Organizing Neuro-Fuzzy Inference System

Cited by 14 publications

References 5 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

Reasons for a careful design of fuzzy sets

Efficiency improvement in multi-sensor wireless network based estimation algorithms for distributed parameter systems with application at the heat transfer

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