Applications of neuro fuzzy systems: A brief review and future outline

Kar, Samarjit; Das, Sujit; Ghosh, Pijush K.

doi:10.1016/j.asoc.2013.10.014

Cited by 299 publications

(108 citation statements)

References 112 publications

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“…(12) is used to convert the complex-valued net-input to real-valued output. The total number of complex-valued membership functions is 3 × 2 = 6 ( 11 , ⋯ , 32 ), and the number of complex-valued antecedent grades is 3 ( 1 , ⋯ , 3 to be determined is (6 × 2 + 3) × 2 = 30.…”

Section: Demonstration Of Proposed Simplified Methods On a Sample mentioning

confidence: 99%

“…Conceiving complementary strengths of neural and fuzzy systems, neuro-fuzzes have been applied to handle numerous real-life problems including control, function approximations, classifications, etc. [1][2][3][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Neuro-fuzzy methods refer to combinations of artificial neural networks and fuzzy models [1][2][3]. Artificial neural networks are the computational models of neuronal cell behaviors in the brain, and have high learning ability as well as parallel processing ability.…”

Section: Introductionmentioning

confidence: 99%

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Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Hata¹,

Akhand²,

Islam³

et al. 2018

IJISA

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Abstract-The conventional real-valued neuro-fuzzy method (RNF) is based on classic fuzzy systems with antecedent membership functions and consequent singletons. Rules in RNF are made by all the combinations of membership functions; thus, the number of rules as well as total parameters increase rapidly with the number of inputs. Although network parameters are relatively less in the recently developed complex-valued neuro-fuzzy (CVNF) and quaternion neuro-fuzzy (QNF), parameters increase with number of inputs. This study investigates simplified fuzzy rules that constrain rapid increment of rules with inputs; and proposed simplified RNF (SRNF), simplified CVNF (SCVNF) and simplified QNF (SQNF) employing the proposed simplified fuzzy rules in conventional methods. The proposed simplified neuro-fuzzy learning methods differ from the conventional methods in their fuzzy rule structures. The methods tune fuzzy rules based on the gradient descent method. The number of rules in these methods are equal to the number of divisions of input space; and hence they require significantly less number of parameters to be tuned. The proposed methods are tested on function approximations and classification problems. They exhibit much less execution time than the conventional counterparts with equivalent accuracy. Due to less number of parameters, the proposed methods can be utilized for the problems (e.g., real-time control of large systems) where the conventional methods are difficult to apply due to time constrain.

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Section: Demonstration Of Proposed Simplified Methods On a Sample mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Hata¹,

Akhand²,

Islam³

et al. 2018

IJISA

View full text Add to dashboard Cite

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“…Recent development of NFSs based on fault diagnosis can be found in Refs. [17,[115][116][117][118][119][120][121][122]. Nevertheless, several issues still need to be considered.…”

Section: Nfssmentioning

confidence: 99%

“…In the last decades, increased attention has been given to computational intelligence methods [16]. While considerable achievements have been acquired, several new computational intelligence techniques, such as fuzzy neural network [17], deep learning network [18], and extreme learning machine (ELM), have been proposed to solve the practical problem [19].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Construction of integrated cloud computing platform for Japanese autonomous learning and examination

Sun

2020

Comp Applic In Engineering

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Regarding autonomous learning, Japan is no special case which characterizes the advancement of Japan's selection tests and school educational plan—especially since World War II—is a particularly focussed spotlight on names, dates, certainties, and figures as opposed to the verbal correspondence of ideas. However, for a couple of excellent establishments, both secondary schools and colleges strenuously stay away from test addresses that approach understudies to develop a composed reaction or even to peruse a section of a huge length. This disregard of the composed word would be inconceivable in social orders like China, Korea, and Vietnam, with their long custom of composing common administration assessments based on the Confucian classics. In this paper, Fuzzy reinforcement Oracle Database Algorithm has been used for changing the information on the board and organization. However, empowering the autonomous database is basic to our prosperity. the autonomous database learning subscription is an all‐computerized learning arrangement which is perfect for cloud directors, cloud engineers, and business experts, who need to get familiar with an autonomous database. This cloud learning subscription shows how the arrangement of a cloud based autonomous database cloud is appropriate for the remaining tasks at hand and a necessity. This is an extraordinary beginning stage for any individual who needs to find out about the main autonomous database in the market.

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Applications of neuro fuzzy systems: A brief review and future outline

Cited by 299 publications

References 112 publications

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Construction of integrated cloud computing platform for Japanese autonomous learning and examination

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