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: 这是一个四个不同的S函数实现集合的递归模糊神经网络（RFNN）。该网络采用了4组可调参数,这使得它非常适合在线学习/操作，从而可应用到系统识别等方面。-This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy network trained exclusively by error backpropagation at layers 2 and 4. The network employs 4 sets of adjustable parameters. In Layer 2: mean[i,j], sigma[i,j] and Theta[i,j] and in Layer 4: Weights w4[m,j]. The network uses considerably less adjustable parameters than ANFIS/CANFIS and therefore, its training is generally faster. This makes it ideal for on-line learning/operation. Also, its approximating/mapping power is increased due to the employment of dynamic elements within Layer 2. Scatter-type and Grid-type methods are selected for input space partitioning. Platform: |
Size: 117760 |
Author:林真天 |
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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: 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: neuro fuzzy networks , application examples with ANFIS tool in predicting the amount of energy that can generate a power plant Platform: |
Size: 5120 |
Author:Cbasmaximo |
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