Description: 使用自适应神经模糊推理系统的方法预测时间序列-The use of Adaptive Neuro-Fuzzy Inference System for Prediction of Time Series Platform: |
Size: 1024 |
Author:陆谨 |
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Description: 本文提出一种新的智能故障诊断方法,基于统计分析,提出了一种改进的距离的评价技术和适应性类神经模糊推论系统(简称ANFIS)。该方法包括三个阶段。 -This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation
technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including
time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy Platform: |
Size: 176128 |
Author:张力 |
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Description: 基于自适应控制的模糊神经网络的仿真及其动态学习-Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference
System (ANFIS) for Mobile Learning Platform: |
Size: 1174528 |
Author:zhangzhenlin |
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Description: APPLICATION OF ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN THE PROCESS OF TRANSPORTATION SUPPORT Platform: |
Size: 768000 |
Author:Dragan |
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Description: Decision support model for prioritizing railway level crossings for safety improvements: Application of adaptive neuro-fuzzy system Platform: |
Size: 563200 |
Author:Dragan |
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Description: Power electronics plays an important role in controlling
the grid-connected renewable energy sources. This paper
presents a novel adaptive neuro-fuzzy control approach for
the renewable interfacing inverter. The main objective is to
achieve smooth bidirectional power flow and nonlinear unbalanced
load compensation simultaneously, where the conventional
proportional-integral controller may fail due to the rapid change
in the dynamics of the highly nonlinear system. The combined
capability of neuro-fuzzy controller in handling the uncertainties
and learning from the processes is proved to be advantageous
while controlling the inverter under fluctuating operating conditions.
The inverter is actively controlled to compensate the harmonics,
reactive power, and the current imbalance of a three-phase
four-wire (3P4W) nonlinear load with generated renewable power
injection into the grid simultaneously. Platform: |
Size: 20480 |
Author:hareesh |
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Description: Abstract— Identifying exceptional students for
scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis) has less error and
can be used as an effective alternative system for classifying students. -Abstract— Identifying exceptional students for
scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis) has less error and
can be used as an effective alternative system for classifying students. Platform: |
Size: 299008 |
Author:Nguyen Anh Tuan |
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Description: In this paper, the application of Adaptive Neuro Fuzzy System (ANFIS) controller is used formitigating the
various Load Frequency Control (LFC) issues in a two area hydrothermal power system under deregulated
environment is highlighted. To improve the LFC performance, combination of Super Conducting Magnetic
Energy Source (SMES) and Thyristor Controlled Phase Shifters (TCPS) are included in its control area. The Platform: |
Size: 1084416 |
Author:Gomaa Haroun |
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Description: In this paper, the application of Adaptive Neuro Fuzzy System (ANFIS) controller is used formitigating the
various Load Frequency Control (LFC) issues in a two area hydrothermal power system under deregulated
environment is highlighted. To improve the LFC performance, combination of Super Conducting Magnetic
Energy Source (SMES) and Thyristor Controlled Phase Shifters (TCPS) are included in its control area. The Platform: |
Size: 1084416 |
Author:Gomaa Haroun |
Hits:
Description: In this paper, the application of Adaptive Neuro Fuzzy System (ANFIS) controller is used formitigating the
various Load Frequency Control (LFC) issues in a two area hydrothermal power system under deregulated
environment is highlighted. To improve the LFC performance, combination of Super Conducting Magnetic
Energy Source (SMES) and Thyristor Controlled Phase Shifters (TCPS) are included in its control area. The Platform: |
Size: 1084416 |
Author:Gomaa Haroun |
Hits:
Search - adaptive neuro fuzzy system