Showing 1-25 of 955 resultsfor "Index Terms":Fuzzy neural network (FNN)
"Index Terms":Fuzzy neural network (FNN)
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Adaptive Fuzzy Neural Network Control Design via a T–S Fuzzy Model for a Robot Manipulator Including Actuator Dynamics
This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, a...Show More
Fuzzy Multiple Hidden Layer Recurrent Neural Control of Nonlinear System Using Terminal Sliding-Mode Controller
IEEE Transactions on Cybernetics
Cited by: Papers (113)
This study designs a fuzzy double hidden layer recurrent neural network (FDHLRNN) controller for a class of nonlinear systems using a terminal sliding-mode control (TSMC). The proposed FDHLRNN is a fully regulated network, which can be simply considered as a combination of a fuzzy neural network (FNN) and a radial basis function neural network (RBF NN) to improve the accuracy of a nonlinear ...Show More
Armed forces knowledge management risk evaluation based on FNN
2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management
Year: 2011 | Conference Paper |
Cited by: Papers (2)
According to the risk theory, propose the definition of armed forces knowledge management risk. On the basis of that, the risk factors of the armed forces knowledge management were analyzed, and the armed forces risk evaluation index system was constructed. Based on the fuzzy feature of the evaluation index, fuzzy mathematics and neural networks were integrated to established the fuzzy neural netw...Show More
Design of DC/DC Boost converter with FNN solar cell Maximum Power Point Tracking controller
2010 5th IEEE Conference on Industrial Electronics and Applications
Year: 2010 | Conference Paper |
Cited by: Papers (5)
This paper demonstrates the Maximum Power Point Tracking (MPPT) controller that uses a DC/DC Boost converter with a Fuzzy Neural Network (FNN) system. It simplifies the topology of the DC/DC boost converter model to state equations, which is easy to simulate with Matlab. Additionally, the FNN system uses an integrated Fuzzy and Neural Network (NN), which advantages include uncertainty ...Show More
DCT-Yager FNN: A Novel Yager-Based Fuzzy Neural Network With the Discrete Clustering Technique
Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into un...Show More
PI and PD Fuzzy Neural Network Controller Basedon Extended Kalman Filter for Brushless Drives
2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
This paper presents development of PI and PD fuzzy neural network (FNN) controller for online speed tracking of brushless drives. This system is implemented by extended kalman filter (EKF) training algorithm to train PI FNN and PD FNN controller. FNN is a learning technique which finds fuzzy logic parameters by initiating techniques from artificial neural networks. Each ...Show More
On the Equivalence of a Table Lookup (TL) Technique and Fuzzy Neural Network (FNN) With Block Pulse Membership Functions (BPMFs) and Its Application to Water Injection Control of an Automobile
This paper presents an alternative method to design a fuzzy neural network (FNN) using a set of nonoverlapped block pulse membership functions (BMPFs), and this FNN with nonoverlapped BPMFs will be shown to be equivalent to the conventional table lookup (TL) technique. Therefore, the hidden links between TL and FNN techniques are revealed in this paper that provides a methodology...Show More
Research on Applications of A New-Type Self-Organization FNN Controller
2007 IEEE International Conference on Automation and Logistics
Year: 2007 | Conference Paper |
A new fuzzy neural network is introduced in this paper which employs self-organization competition neural network to optimize the structure of the fuzzy neural network., and applies a genetic algorithm to adjust the connection weights of the fuzzy neural network so as to get the best structure and weights of the fuzzy neural network. Simulations are made when the pole becomes 2 meters and the rand...Show More
Adaptive behavior design based on FNN for the mobile robot
2009 IEEE International Conference on Automation and Logistics
Year: 2009 | Conference Paper |
A fuzzy neural network (FNN) has been trained off-line to memory the fuzzy control rules of the adaptive behaviors for the local optimal path planning of mobile robot. The fuzzy rules were collected automatically by reinforcement Q_Learning (QL) on-line beforehand. This method has overcome the disadvantage of traditional means which are determined by artificial experience, and are able to me...Show More
Large Tanker Motion Model Identification Using Generalized Ellipsoidal Basis Function-Based Fuzzy Neural Networks
IEEE Transactions on Cybernetics
Cited by: Papers (68)
In this paper, the motion dynamics of a large tanker is modeled by the generalized ellipsoidal function-based fuzzy neural network (GEBF-FNN). The reference model of tanker motion dynamics in the form of nonlinear difference equations is established to generate training data samples for the GEBF-FNN algorithm which begins with no hidden neuron. In the sequel, fuzzy rules associated wit...Show More
A new method of image data fusion based on FNN
2010 Sixth International Conference on Natural Computation
Year: 2010 | Conference Paper |
The image data are obtained by a variety of multimedia, information equipment, which include amount of information, extensive coverage, and redundancy in ubiquitous computing paradigm. In order to make use of these information reasonably and efficiently, it is necessary to fuse such massive data, therefore the multi-sensor image information fusion become a key technology to ubiquitous computing. A...Show More
Performance evaluation of time delay processes based on FL, FNN & fractional order controllers
Michael Faraday IET International Summit 2015
Year: 2015 | Conference Paper |
Time-delay usually appears in any practical system like electrical, mechanical, chemical processes and it has a prominent outcome on system operation. This paper introduces an improved design method of different types of intelligent integer type and fractional type PID controllers and their comparison with conventional PID controllers is discussed here to illustrate the superiority of the designed...Show More
Fuzzy Neural Network Model and Its Application in Water Quality Evaluation
2009 International Conference on Environmental Science and Information Application Technology
Year: 2009 | Conference Paper |
Cited by: Papers (2)
In view of the defect of traditional water quality evaluation model, based on fuzzy neural network theory, a new model of fuzzy neural network (FNN) comprehensive evaluation is developed to evaluate surface water quality in Suzhou. Fuzzy neural network is a new type neural network consisting radical basis network and compete neural network, which is simple in structure, easy for training and...Show More
Comparing the performance of ANN with FNN on mammography mass data set
2014 IEEE International Advance Computing Conference (IACC)
Year: 2014 | Conference Paper |
Cited by: Papers (2)
Nowadays soft computing techniques such as fuzzy logic, artificial neural network and neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. These diagnosing systems help in early detection of diseases and assist the patient to get proper medication in time. In this paper, the artificial neural network such as multilayer perceptron neural network and radia...Show More
Fault diagnosis of underwater vehicle with FNN
Proceedings of the 10th World Congress on Intelligent Control and Automation
Year: 2012 | Conference Paper |
Cited by: Papers (7)
Aiming at the character that the uncertainties of the complex system of underwater vehicle (UV) bring to model the system very difficult, a fuzzy neural network (FNN) with least adjustment is proposed to construct the motion model of UV. The adjustment of the dynamic learning rate and weights of FNN is studied. The FNN has the ability not only to approach the whole figure of a fu...Show More
Fuzzy neural network integrated with PCA and its application in raw meal grinding process
2010 Chinese Control and Decision Conference
Year: 2010 | Conference Paper |
A fuzzy neural network model has been proposed and successfully applied to an annual clinker production capacity of 0.73 million ton of Jiuganghongda Cement Plant in China. Because the measurement values from raw meal grinding process are not independent, data sets with higher dimension increased model structure. Thus, a novel method based on fuzzy neural network(FNN) and principal component...Show More
Control of Mobile Robot Using Prediction-based FNN
2009 International Joint Conference on Artificial Intelligence
Year: 2009 | Conference Paper |
A prediction model-based fuzzy neural network (PFNN) approach is proposed, in which a basic FNN is created at first to predict the relative position of the trajectory. Then a FNN is used independently to get the control values of the variables for motor motion according to those variables including trajectory position both from those measured and predicted values, and those speed varia...Show More
Simplified Interval Type-2 Fuzzy Neural Networks
This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK...Show More
Fault Diagnosis of Ship Main Power System Based on Multi-Layer Fuzzy Neural Network
2006 6th World Congress on Intelligent Control and Automation
Year: 2006 | Conference Paper |
Cited by: Papers (3)
Artificial neural network (ANN) has been successfully applied to fault diagnosis systems in real-world applications. But only single network is used for diagnosis, which is not good at handling expert knowledge. Multiple faults in complex systems occur commonly in practice. When the single network is used to deal with complicated problems of fault diagnosis, it'll be so gigantic that a series of d...Show More
Chaos synchronization between two different chaotic systems using adaptive interval type-2 FNN control
2010 International Symposium on Computer, Communication, Control and Automation (3CA)
Year: 2010 | Conference Paper |
A new adaptive interval type-2 fuzzy neural network (FNN) control for chaos synchronization between two different chaotic systems is proposed to handle the training data corrupted by noise or rule uncertainties involving external disturbances. Adaptive interval type-2 FNN control scheme and H∞ tracking approach are incorporated to synchronize two different chaotic systems and the effec...Show More
Fuzzy neural networks pattern recognition method and its application in ultrasonic detection for bonding defect of thin composite materials
2009 IEEE International Conference on Automation and Logistics
Year: 2009 | Conference Paper |
Cited by: Papers (5)
Aiming at the problems in pattern recognition of bonding defect of thin composite materials, a new fuzzy neural network (FNN) pattern recognition method was proposed via taking full advantage of processing fuzzy information of the fuzzy pattern recognition and self-learning of the neural network (NN) pattern recognition. The structure characteristics and realization approach of the algorithm...Show More
Research on Fuzzy Neural Networks Model and its Application in Predicting the Precipitation State
2006 6th World Congress on Intelligent Control and Automation
Year: 2006 | Conference Paper |
The self-adaptation and self-studying features of neural networks have been combined with the logical reasoning ability of fuzzy system to produce a fuzzy system based neural networks, BP arithmetic is used to adjust system parameters. Finally the fuzzy neural networks model with the monthly mean rainfall is established, as compared with the model of multi-variant linear regression, the fuzzy neur...Show More
A Time Series Classification Method Based on 1DCNN-FNN
2021 33rd Chinese Control and Decision Conference (CCDC)
Year: 2021 | Conference Paper |
Cited by: Papers (7)
With the rise of deep learning technology, the use of one-dimensional convolutional neural network (1DCNN) to process time series has the advantages of higher classification accuracy and stronger generalization ability. However, the 1DCNN constructs a classification model by identifying the feature vector of the data distribution, which lacks the reasoning ability on digital features. Because Fuzz...Show More
Feedforward Decoupling Intelligent Algorithm for Multivariable Based on FNN and Its Application in Biology Fermentation Control
2010 International Conference on Electrical and Control Engineering
Year: 2010 | Conference Paper |
The coupling relations of all variables during fermentation process with fermentation are discussed. Since the parameters are always changing as time-varying, nonlinearity and randomicity during biology fermentation control process. the scheme for biology fermentation control process using feed forward decoupling intelligent algorithm for multivariable based on FNN is presented. Fuzzy-Neural...Show More
Soft sensor modeling based on GD-FNN for microbial fermentation process
IET International Conference on Information Science and Control Engineering 2012 (ICISCE 2012)
Year: 2012 | Conference Paper |
In order to solve the problems in real-time measurement of crucial biological parameters (such as biomass concentration, substrate concentration, product concentration, etc.) in the microbial fermentation process, a soft sensor modeling method based on Generalized Dynamic Fuzzy Neural Network (GD-FNN) is proposed. Taking the penicillin fermentation process as an example, initially, the auxil...Show More
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