[PDF] Top 20 Fuzzy Modeling using Neural Gas
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Fuzzy Modeling using Neural Gas
... simplified fuzzy model is more effective than the similar approach for TS fuzzy ...method using generalized inverse matrix to determine the initial assignment of weight parameters ...of fuzzy ... See full document
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Fermentation process modeling of exopolysaccharide using neural networks and fuzzy systems with entropy criterion
... three modeling methods: MLP network model, RBF network model and TSK fuzzy system ...for modeling are generated by carrying out a number of fermentation runs under various input ...RBF neural ... See full document
9
Applying Fuzzy Logic Model for Bending Rigidity Evaluation of Woven Fabrics
... artificial neural network model, ...fabrics using fuzzy ...of fuzzy model to predict bending rigidity based on the mentioned ...of fuzzy logic model in comparison with three ... See full document
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Modeling of Stripper Temperature based on Improved T-S Fuzzy Neural Network
... During the process of PVC stripping, due to the accuracy of measurement instruments, methods employed and human factors, various errors can not be avoided, which will affect process modeling and control quality. ... See full document
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A Solution to the Problem of Extrapolation in Car Following Modeling Using an online fuzzy Neural Network
... Modeling of car following behaviors of the drivers in a traffic flow, is an important task in the field of microscopic traffic modeling. The car following models (CFM) play a key role in intelligent ... See full document
9
Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods
... the periodogram of raw data of random outputs of the DTG. Because of using a wind-up anti aliasing Ulter, for data acquisition, there is no frequency more then 50 Hz. In fact, the high frequency noises are omitted ... See full document
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Volume 8 | Issue 2 - 2018
... fuzzy neural systems is in most cases time-consuming, and it limits the use of small real-time data sets in deriving an individualized and reliable patient ... See full document
5
Group Method of Data Handling for Modeling Magnetorheological Dampers
... System modeling is instrumental for designing new proc- esses, analyzing existing processes, designing controllers, optimizations, supervision, and fault detection and diag- ...and modeling [1]. The choice ... See full document
10
Artificial Intelligence Based Fault Diagnosis of Power Transformer-A Probabilistic Neural Network and Interval Type-2 Support Vector Machine Approach
... probabilistic neural network (PNN) and Interval Type-2 Fuzzy Support Vector Machine ...probabilistic neural network ...classified using AI ...Probabilistic Neural Network (PNN), ... See full document
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A NEURAL FUZZY APPROACH TO MODELING THE THERMAL BEHAVIOR OF POWER TRANSFORMERS
... of neural networks to learn about their environment and to adaptively fine- tune their parameters to improve the systems’ performance is one of their strong ...allows neural networks to be used in a variety ... See full document
139
Modeling and Optimization of Energy Inputs and Greenhouse Gas Emissions for Eggplant Production Using Artificial Neural Network and Multi-Objective Genetic Algorithm
... biological neural system and are used to solve a wide variety of problems in science and engineering, particularly for some areas where the conventional modelling methods ...the modeling of energy ... See full document
12
Modelling Metal cutting Parameters Using Intelligent Techniques
... the modeling of metal ...artificial neural networks and hybrid, neuro-fuzzy model in the prediction of a workpiece temperature and surface ... See full document
11
Design and development of artificial intelligence system for weather forecasting using Soft Computing Techniques
... statistical modeling and evaluation. However, modeling is shown to be a successful method to forecast weather parameters by using different types of Soft Computing Techniques such as Neural ... See full document
5
Wheat Yield Prediction Using Artificial Neural Network and Crop Prediction Techniques (A Survey)
... artificial neural networks (ANN) to analyze consumer behavior and to model the consumer decision-making process ...index–AORD using multi-layer feed- forward neural networks from the time series data ... See full document
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Title: Relevance of Neural Network in Fault Detection of Single Phase Induction Motor
... A neural network is the artificial way of trying to simulate the brain electronically. Human brain are made up of about 100 billion tiny units called Neurons. Each neuron is connected to other neurons and ... See full document
11
Modelling the formation of Ozone in the air by using Adaptive Neuro-Fuzzy Inference System (ANFIS) (Case study: city of Yazd, Iran)
... The impact of air pollution and environmental issues on public health is one of the main topics studied in many cities around the world. Ozone is a greenhouse gas that contributes to global climate. This study was ... See full document
5
Deduction of reservoir operating rules for application in global hydrological models
... evolution metropolis (SCEM-UA) method (Vrugt et al., 2003) to calculate the optimal release within a predetermined daily feasible release range, based on the reservoir purpose. Adam et al. (2007) use this algorithm to ... See full document
21
Highly nonlinear control of a solar thermal power plant using soft computing fuzzy tuning techniques
... Mamdani-type fuzzy incremental controller (Loebis, 2000), which due to its simplicity, and use over the whole operating range of the plant, required a relatively large number of fuzzy sets, membership ... See full document
8
A Review on: Artificial intelligence techniques in electrical and computer engineering
... acquired using a curve fitting algorithm that takes for granted an essential frequency accurately equal to the power system frequency at the same time as the disturbance waveform, computed as a difference with the ... See full document
6
Modeling of ipm synchronous generator by using fuzzy logic controller under variable speed
... scheme. Fuzzy logic adds to bivalent logic an capability to reason precisely with imperfect ...In fuzzy logic, results of reasoning are expected to be provably valid for ... See full document
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