neural fuzzy inference network
Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis
... of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault de- tection/diagnosis process and successful fault detection/diagnosis schemes can be ...two ...
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Adaptive neural network fuzzy inference system for HFC processes
... The advantages of the second system consist in the repeatability and flexibility of the process, as well as in the non-linear behaviour of output data generated within the same class, due to the way in which the ...
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Forecasting of Intellectual Capital by Measuring Innovation Using Adaptive Neuro-Fuzzy Inference System
... Artificial Neural Networks (ANNs) have earned themselves an excellent reputation as non-linear ...the neural networks have been accused that they are not being able to recognize the degree to which an input ...
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Calculation of Resonant Frequency for a Microstrip Antenna with Vertical Slots Using Applying Adaptive Network-Based Fuzzy Inference System
... Figure 11.below illustrates S11 curves for the whole possible situations Since the antenna resonates in more than one frequency ,it is necessary that an appropriate algorithm to detect the resonant frequencies be ...
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Computing air demand using the Takagi–Sugeno model for dam outlets
... neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet ...Levenberg-Marquardt neural network (LMNN) and ...
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Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System
... allows fuzzy systems to learn from the data that are modelled Because of the linear dependence of each rule on the input variables of a system, the Sugeno method is ideal for acting as an interpolating supervisor ...
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Modelling tide prediction using linear model and adaptive neuro fuzzy inference system (ANFIS) in Semarang, Indonesia
... and fuzzy inference system (FIS) where neural network algorithm is used to determine FIS parameter (Chang and Lain, ...Sugeno’s fuzzy model is a sistematic approach in generating ...
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Study of Existing Work on Soft Computing Methodologies and Fusion of Neural Network and Fuzzy Logic for Estimation and Approximation
... Neuro-Fuzzy Inference System (ANFIS) and a Kernel system to solve the problem of predicting rush orders for regulating the capacity reservation mechanism in ...than Fuzzy logic for the prediction of ...
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Neuro fuzzy Modeling of an Eco friendly Melting Furnace Parameters using Bio fuels for the Agile Production of Quality Castings
... (TSK) fuzzy model [9]. ANFIS represents a neural network approach to the design of fuzzy inference ...ANFIS network makes use of a supervised learning algorithm to determine a ...
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Intelligent controllers for velocity tracking of two wheeled inverted pendulum mobile robot
... TWIP. Fuzzy Logic Control (FLC), Neural Network Inverse Model control (NN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were designed and simulated on the TWIP ...
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Implementation of Computational Intelligent Techniques for Diagnosis of Cancer Using Digital Signal Processor
... like Fuzzy Logic, Adoptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network for diagnosis of cancer using TMS320C6713 (Texas Instruments) DSP (Digital Signal ...
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Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network
... artificial neural network model. Caglar et al. [24] applied the neural network to dynamic analysis of reinforced concrete ...developed Fuzzy Inference Systems (FIS) and applied ...
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An Adaptive Neural Network Fuzzy Inference Controller Using Predictive Evolutionary Tuning
... Many approaches have been suggested for nonlinear system control; the problem becomes more complex when uncertainties and noise are considered. One approach that has gained success when the system model is complex or ...
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Electricity Price Forecasting using Asymmetric Fuzzy Neural Network Systems
... Gaussian Fuzzy Inference Neural Network (AGFINN) is presented an alternative neurofuzzy modelling ...(NF) network shown in Fig 1 consists of five ...
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International Journal of Emerging Technology and Advanced Engineering
... Propagation Neural Network (BPNN), and Dynamic Fuzzy Inference System ...propagation neural network is implemented to embed and extract the watermark in one method, while the ...
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Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches
... An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in ...
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Estimation of pH and MLSS using Neural Network
... neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN) modeling applied to the domestic plant of the Bunus regional sewage treatment ...forward neural ...
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Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method
... neuro-fuzzy inference system which connects fuzzy logic system with neural network and constructs a hybrid intelligent system and benefits from the advantages of both fuzzy logic ...
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A NEURAL FUZZY APPROACH TO MODELING THE THERMAL BEHAVIOR OF POWER TRANSFORMERS
... Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) are chosen to compare with the conventional Institute ...
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Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis
... PANFIS is compared against three prominent algorithms, known as eTS [2], simp_eTS [12], and ANFIS [1]. eTS and simp_eTS are counterparts of PANFIS, which features both structural and parameter learning scenarios in the ...
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