[PDF] Top 20 KNOWLEDGE LEARNING ON FUZZY EXPERT NEURAL NETWORKS
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KNOWLEDGE LEARNING ON FUZZY EXPERT NEURAL NETWORKS
... However, since the knowledge of fuzzy rules learned by the CBP training is distributed over the weights on the input links of OR nodes, there may be rules with identical antecedents and [r] ... See full document
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Precise image alignment using cooperative neural-fuzzy networks with association rule mining-based evolutionary learning algorithm
... The problem of precise image alignment has been well studied in several fields. In Ref. 1, Liu et al. point out that image alignment techniques are broadly classified as feature-based 2,3 and area-based matching ... See full document
16
Dynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithm
... optimal learning rate, in the sense of maximum error reduction, for each iteration in the training (back propagation) process can therefore be found for this two-layer ...stable learning rate for this ... See full document
9
Simplified Interval Type-2 Fuzzy Neural Networks
... Type-2 Fuzzy Neural Networks Yang-Yin Lin, Shih-Hui Liao, Jyh-Yeong Chang, Member, IEEE, and Chin-Teng Lin, Fellow, IEEE Abstract— This paper describes a self-evolving interval type-2 fuzzy ... See full document
11
Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution
... This study proposes a modified differential evolution (MODE) for an ANFN (ANFN-MODE). The proposed MODE learning algorithm has two crucial ideas. First, MODE adopts a method to effectively search between the ... See full document
15
The Algorithmic Parameters of a Fuzzy Dynamic Learning Neural Network
... It is also meaningful to compare the FDL with DL by applying both neural networks to SAR image classification. Both the DL and FDL have the same configurations as stated above. As shown in Table III, by ... See full document
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A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision
... So, it provides an assistant feature between the edge and noise pixels. In order to train the proposed NN for noise detection, the 512 × 512 of the gray-scale Lena image with 20% of impulse noise generated uniformly ... See full document
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Tracking a near-field moving target using fuzzy neural networks
... of fuzzy rules can be arbitrarily ...of fuzzy rules. The fuzzy sets of each fuzzy rule can be designed without considering other fuzzy ...the fuzzy sets. Instead of consulting ... See full document
13
Identification and control of dynamic systems using recurrent fuzzy neural networks
... In this paper, the proposed RFNN, which is a modified version of the FNN, is used to identify and control a nonlinear dynamic system. The RFNN is a recurrent multilayered connectionist network for realizing fuzzy ... See full document
18
Noisy speech segmentation/enhancement with multiband analysis and neural fuzzy networks
... ∗ [email protected] This paper addresses the problem of speech segmentation and enhancement in the pre- sence of noise. We first propose a new word boundary detection algorithm by using a neural ... See full document
29
Machine learning with parallel neural networks for analyzing and forecasting electricity demand
... Neural network (NN) methods have a property of universal approximation and can achieve high predictive accuracy, therefore, they have received a lot of attention in decision-support systems. Although being ... See full document
29
Fuzzy perceptron neural networks for classifiers with numerical data and linguistic rules as inputs
... most learning procedures utilize and process numerical data only, ...classification knowledge, especially concerning the nature of patterns in a set, can be included as a part of inputs and then learned by ... See full document
16
Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks
... algorithm-based neural fuzzy de- cision tree (GANFDT) to realize the mixed scheduling scheme ...efficiently. Neural networks and fuzzy systems have been ap- plied for ATM traffic ... See full document
14
The Bounded Capacity of Fuzzy Neural Networks (FNNs) Via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) With Its Applications
... and fuzzy rule layer are not fully connected, and the operators of the fuzzy rule layer of the FNN are the product form rather than the adding form in an ...connected neural fuzzy inference ... See full document
14
A QoS-provisioning neural fuzzy connection admission controller for multimedia high-speed networks
... a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed ...networks. Fuzzy logic systems have been successfully applied to deal with ... See full document
11
Protein metal binding residue prediction based on neural networks
... machine learning techniques including self-organized maps (SOM), artificial neural networks (ANNs), sup- port vector machine (SVM), and fuzzy logic have obtained great success in many fields in ... See full document
15
Efficient immune-based particle swarm optimization learning for neuro-fuzzy networks design
... 20. C. J. Lin and W. H. Ho, “An asymmetry-similarity-measure-based neural fuzzy in- ference system,” Fuzzy Sets and Systems, Vol. 152, 2005, pp. 535-551. Cheng-Jian Lin (林正堅) received the B.S. degree ... See full document
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REINFORCEMENT STRUCTURE PARAMETER LEARNING FOR NEURAL-NETWORK-BASED FUZZY-LOGIC CONTROL-SYSTEMS
... After the consequents of rule nodes are determined for both the action and evaluation networks (i.e., when the structure leaming process is done and the structure will not be [r] ... See full document
18
Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network
... adaptive fuzzy control [16] of nonlinear systems, it is usu- ally classified into two categories: indirect adaptive control and direct adaptive ...The fuzzy system is used to describe the plant ... See full document
12
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
... of learning the relation- ship between any data pairs. Fuzzy theories are based on the way how brains deal with inexact ...while fuzzy systems often deal with issues such as rea- soning at a ... See full document
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