NN-driven fuzzy reasoning

Tokuda, Hideyuki; Hayashi, Isao

doi:10.1016/0888-613x(91)90008-a

Cited by 516 publications

(129 citation statements)

References 6 publications

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“…"Back-propagation" learning algorithm is an effective learning algorithm of multilayer perceptron and has directed attention to the information processing capability of artificial neural networks. This algorithm had been widely employed for various pattern classifications or inference problems expressed in terms of nonlinear functions [13] .…”

Section: Defuzzification Proceduresmentioning

confidence: 99%

Study of the bullwhip effect in Chinese coal supply chain under fuzzy environment

Yun

Zhang

2016

MATEC Web Conf.

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Abstract. Supply chain management is important for coal companies and organizations to improve their business and enhance competitiveness in the Chinese marketplace. The bullwhip effect problem of coal supply chain systems with all demands, lead times, and ordering quantities in an uncertain environment is addressed in this paper. To simulate the bullwhip effect, the Hong Fuzzy Time Series approach and Genetic Algorithm module are preferred as a superior forecasting model. And then a back propagation Neural Network module is added to defuzzify the output of the proposed model. So the bullwhip effect is calculated and analyzed here. The effectiveness and flexibility of proposed method is verified through simulation study.

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Section: Defuzzification Proceduresmentioning

confidence: 99%

Study of the bullwhip effect in Chinese coal supply chain under fuzzy environment

Yun

Zhang

2016

MATEC Web Conf.

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“…This kind of inclusion is application oriented and appropriate for control and pattern recognition applications both. The worthy example of hybrid neuro fuzzy are GARIC, ARIC, ANFIS the NNDFR model [22,23,18,38,17]. …”

Section: Fuzzy Neural Hybrid Systemmentioning

confidence: 99%

Adaptive Neural Fuzzy Inference System for Employability Assessment

Kumari¹,

Sharma²,

Kumar³

2014

IJCATR

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Employability is potential of a person for gaining and maintains employment. Employability is measure through the education, personal development and understanding power. Employability is not the similar as ahead a graduate job, moderately it implies something almost the capacity of the graduate to function in an employment and be capable to move between jobs, therefore remaining employable through their life. This paper introduced a new adaptive neural fuzzy inference system for assessment of employability with the help of some neuro fuzzy rules. The purpose and scope of this research is to examine the level of employability. The concern research use both fuzzy inference systems and artificial neural network which is known as neuro fuzzy technique for solve the problem of employability assessment. This paper use three employability skills as input and find a crisp value as output which indicates the glassy of employee. It uses twenty seven neuro fuzzy rules, with the help of Sugeno type inference in Mat-lab and finds single value output. The proposed system is named as Adaptive Neural Fuzzy Inference System for Employability Assessment (ANFISEA).

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“…which is widely used by Takagi and Hayashi [6], Sugeno and Kang [7], and Kondo [8] to test their modelling approaches. Table 3 shows 40 pairs of the input-output data obtained from (4) [9].…”

Section: -Input Nonlinear Systemmentioning

confidence: 99%

Use of an Evolutionary Inductive Self-Organizing Network for Uncertain Nonlinear and Robotic Systems

Kim

Seo

Kim

2012

International Journal of Advanced Robotic Systems

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We discuss a new design methodology for an inductive self-organizing network using an evolutionary algorithm and its practical applications. The inductive self-organizing network centres on the idea of a group method for data handling. The performances of this network depend strongly on the number of input variables available to the model and the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must be used with its heavy computation burden and low efficiency. Moreover it does not guarantee that the obtained model is the best one. In this paper, we propose an evolutionary inductive selforganizing network to alleviate these problems. The order of the polynomial, the number of input variables and the optimum input variables are encoded as a chromosome and the fitness of each chromosome is computed. So the appropriate information for each node is evolved accordingly and tuned gradually throughout the genetic iterations. We can show that the proposed model is a sophisticated and versatile architecture which can construct models for limited data sets, as well as heavy complex robotic systems.

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NN-driven fuzzy reasoning

Cited by 516 publications

References 6 publications

Study of the bullwhip effect in Chinese coal supply chain under fuzzy environment

Study of the bullwhip effect in Chinese coal supply chain under fuzzy environment

Adaptive Neural Fuzzy Inference System for Employability Assessment

Use of an Evolutionary Inductive Self-Organizing Network for Uncertain Nonlinear and Robotic Systems

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