Neuro-fuzzy modeling and control

Jang, Jyh-Shing Roger; Sun, Chuen-Tsai

doi:10.1109/5.364486

Cited by 1,972 publications

(835 citation statements)

References 59 publications

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“…This problem is more distinct if there are multiple inputs, and each of which has numerous fuzzy levels. ANN, on the contrary, is not capable to take linguistic information (fuzzy rules) from human experts [8].…”

Section: Fuzzy Logic and Artificial Neural Networkmentioning

confidence: 99%

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Automatic Handover Decision in Content Centric Networking

Luo

Eymann

et al. 2012

Information and Communication Technologies

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Abstract. The current Internet architecture was designed more than 30 years ago for a very different set of services than those used today. Several new architectures have been proposed for a Future Internet to better meet todays and future requirements. Content Centric Networking (CCN) is one of those new architectures gaining worldwide attention by researchers and the focus of this article. CCN is based on naming content instead of hosts allowing routers to cache popular content. It has been shown that CCN can also support point-to-point real-time conversations, for example voice or video calls. However, it has not been defined how node mobility can be achieved in such a real-time scenario with strong time constraints. In this paper a close study to the handover algorithms has been carried out. A handover decision maker, which is based on neuro-fuzzy system (NFS) is proposed and evaluated. The results show that the proposed decision maker meets the design targets.

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Section: Fuzzy Logic and Artificial Neural Networkmentioning

confidence: 99%

“…NFS is a combination of neural networks and fuzzy logic, and aims to take advantage of both [8]. Generally the process of a NFS consists of two steps: as the first step, the structure of the inputs is learned by the system to decide the fuzzy rule set.…”

Section: Neuro-fuzzy Systemmentioning

confidence: 99%

Automatic Handover Decision in Content Centric Networking

Luo

Eymann

et al. 2012

Information and Communication Technologies

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“…Many important features of ANFIS can support the system to achieve a task intensely; these features are considered as fast and accurate learning, easy to implement, excellent explanation facilities, strong generalization abilities, through fuzzy rules. It is easy to integrate both linguistic and numeric acquaintance for problem solving [18,38,39,13,14,15]. This system is measured as an adaptive fuzzy inference system through the competency of learning fuzzy rules from data and as a connectionist manner provided with linguistic significance.…”

Section: Anfis Structurementioning

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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“…Adaptive neuro-fuzzy system (ANFS) is a hybrid system incorporating the learning abilities of ANN and excellent knowledge representation and inference capabilities of fuzzy logic (Jang, 1993;Jang et al, 1995;Lin et al, 1991) that have the ability to self modify their membership function to achieve a desired performance. An adaptive network, which subsumes almost all kinds of neural network paradigms, can be adopted to interpret the fuzzy inference system.…”

Section: Adaptive Neuro-fuzzy System Based Classifiermentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

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Neuro-fuzzy modeling and control

Cited by 1,972 publications

References 59 publications

Automatic Handover Decision in Content Centric Networking

Automatic Handover Decision in Content Centric Networking

Adaptive Neural Fuzzy Inference System for Employability Assessment

Power quality event classification: an overview and key issues

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