neuro-fuzzy modeling
Neuro Fuzzy Modeling of Design Parameters of Connecting Rod
... a Neuro- Fuzzy system, which combines reasoning ability of FL and learning ability of ANN for modeling the design parameters of CR, to make it more useful for the stress-strain ...of ...
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A New Approach to Adaptive Neuro-fuzzy Modeling using Kernel based Clustering
... for fuzzy model identification or fuzzy modelling for apprehending the system behavior in the form of fuzzy if-then rules based on experimental ...data. Fuzzy c-Means (FCM) clustering and ...
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Adaptive Neuro-Fuzzy Systems
... proposed modeling methodology, which supports this feature at different ...linguistic fuzzy rules, which is common to all neuro-fuzzy ...a fuzzy model can become limited with too many ...
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Adaptive Neuro-Fuzzy Inference System For Rainfall-Runoff Modeling
... Neuro-fuzzy modeling refers to the way of applying various learning techniques developed in the neural network literature to fuzzy modeling or to a fuzzy inference system ...of ...
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A Neuro Fuzzy System for Modeling the Depression Data
... of Fuzzy-C Means (FCM) and K-means (KM) clustering techniques were used for classifying depression grades using Beck’s Depression Inventory-II (BDI-II) ...depression, neuro-fuzzy modeling has ...
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Optimization of functional Spirulina cookies on their sensory attributes
... advanced Neuro- Fuzzy ...Adaptive Neuro-Fuzzy Inference System (ANFIS) called ...adaptive Neuro fuzzy inference system (CANFIS) which can send multi control signals to the ...
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A Neuro fuzzy Computing Technique for Modeling the Acoustic Form Function of Immersed Tubes
... Sugeno fuzzy model [29]; [30] since the consequent part of this FIS is a linear equation and the parameters can be estimated by combination of the gradient descent method and the least squares estimate (LSE), for ...
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Neuro Fuzzy approach for the predictions of economic crisis
... Neuro-Fuzzy approach for the predictions of economic crisis Giovanis, Eleftherios.[r] ...
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A general neuro space mapping technique for microwave device modeling
... model, Neuro- SM with the output mapping [13], dynamic Neuro-SM [14] with 5 delay buffers and 30 hidden neurons, and the pro- posed general Neuro-SM model with 5 delay buffers and 30 hidden neurons ...
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Modeling and control of 6 dof of industrial robot by using neuro fuzzy controller
... The parameters associated with the membership functions changes through the learning process. The computation of these parameters (or their adjustment) is facilitated by a gradient vector. This gradient vector provides a ...
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Utilization of new computational intelligence methods to estimate daily Evapotranspiration of wheat using Gamma pre processing
... First, the normalization on the data is done in line with the following expression. There are two main advantages to normalize features before applying AI methods for prediction. One advantage is to avoid using ...
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A Study & Survey on Rainfall Prediction And Production of Crops Using Data Mining Techniques
... various fuzzy membership generation algorithms can be used: Learning Vector Quantization (LVQ), Fuzzy Kohonen Partitioning (FKP) or Discrete Incremental Clustering ...the fuzzy rules. Neuro ...
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ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR OKRA YIELD PREDICTION
... tions and weather shifts at different loca- tions and areas. There is a need to develop area specific forecasting models based on time series data to help the policy makers for taking effective decisions to counter ad- ...
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Threshold Neuro Fuzzy Expert System for Diagnosis of Breast Cancer
... Genetic Neuro Fuzzy model arsit the radiologist in the diagnosis of breast cancer 2008 ...Advanced Fuzzy – Neuro approach is implemented using compact genetic algorithm and steady state ...
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LAYOUT AN INEXPENSIVE ELLIPTICAL POLARIZED PRODUCTIVE INTEGRATED TRANSCEIVER
... Type-1 Fuzzy Model and Type-2 Fuzzy Model were developed to predict compressive strength of ...different fuzzy model on the performance of concrete strength prediction has been ...Type-2 Fuzzy ...
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The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
... the fuzzy logic and neural networks, are widely spread in real world problems with high effectiveness and versatility for different kinds of ...The fuzzy logic systems with their ability of tackling ...
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Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation
... type-2 fuzzy logic in which the gradient descend algorithm and the neuro- fuzzy-genetic hybrid approach have been used in order to teach the type-2 fuzzy ...with neuro-fuzzy and ...
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Modeling reservoir water release decision using adaptive neuro fuzzy inference system
... The application of ANFIS has been widely used in modeling complex reservoir operations and predictions. Chang and Chang (2006) presented a neuro fuzzy hybrid approach to develop a system for water ...
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A Neuro Fuzzy Approach in the Prediction of Financial Stability and Distress Periods
... A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods Giovanis, eleftheios.[r] ...
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Quantitative Analysis of Cardiac Q Wave Modeling Using Adaptive Neuro Fuzzy Inference System
... clearly modeling the Q wave and providing a crisp output in the form a Q-wave index value which should be able to provide a comparable estimate of cardiac pathological ...
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