Fuzzy feed-back neural network (FFNN)
Solution of two-point fuzzy boundary value problems by fuzzy neural networks
... second-order fuzzy differential equations. This method based on the fully fuzzy neural network to find the numerical solution of the two-point fuzzy boundary value problems for the ...
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Estimation of pH and MLSS using Neural Network
... and feed-forward neural network (FFNN) modeling applied to the domestic plant of the Bunus regional sewage treatment ...and feed- forward neural network techniques as nonlinear ...
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Comparison of Artificial Intelligence Techniques for river flow forecasting
... Neuro Fuzzy Inference Sys- tem (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and ...
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New Numerical Approach for Solving Fuzzy Boundary Value Problems
... order fuzzy differential equations. This method based on the fully fuzzy neural network to find the numerical solution of the two- point fuzzy boundary value problems for the ordinary ...
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Content Based Image Retrieval for Medical Imaging Using Fuzzy FFBP Neural Network Approach
... using fuzzy-feed forward back-propagation neural network ...using fuzzy edge ...the neural network. Feed Forward back propagation neural ...
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Utilizing a new feed-back fuzzy neural network for solving a system of fuzzy equations
... of feed-back neural net- works to obtain an approximate real solution of fuzzy equations system (if ...purposed fuzzy feed-back neural net- work, the connection ...
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Crop Cost Forecasting using Artificial Neural Network with feed forward back propagation method for Mysore Region
... Researchers are continuously developing the new methods in weather and crop yield forecasting. Some common machine learning schemes utilized for predicting weather and crop yield are Fuzzy-C-Means (FCM), Self- ...
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KNOWLEDGE-BASED NEURAL NETWORK FOR LINE FLOW CONTINGENCY SELECTION AND RANKING
... using fuzzy logic approach [15,16], thereby increasing the input dimension to ...three-layered feed- forward MLP model (Rosenblatts, 1957) having 18 input neurons, 9 hidden neurons, and 5 output neurons, ...
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Comparative study of static and dynamic neural network models for nonlinear time series forecasting
... decades, neural network models have been focused upon by researchers due to their more real performance and on this basis different types of these models have been used in ...dynamic neural ...
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A Neuro-Fuzzy Based System for the Classification of Cells as Cancerous or Non-Cancerous
... artificial neural network (ANN) model [26]. A feed-forward ANN was used to construct the survival model; several ANN were combined into a single prediction ...
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Price Prediction of Stock Market using Hybrid Model of Artificial Intelligence
... the Fuzzy-neural system we just need to simply show the correct output for the given ...the network will learn to ignore any inputs that don’t contribute to the output [5, ...is Fuzzy ...
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Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
... as neural network models, in modeling and forecasting the market indexes can yield impressive results (Aladag et ...as neural network models, is a response to the lack of consensus on ...
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Computing air demand using the Takagi–Sugeno model for dam outlets
... combining back-propagation and least square estimate was adopted to identify linear and non-linear parameters in the ANFIS ...The feed-forward Levenberg-Marquardt neural network (LMNN) and ...
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REVIEW PAPER ON AN IMPROVED APPROACH FOR BUSINESS AND MARKET INTELLIGENCE USING ARTIFICIAL NEURAL NETWORK
... We all know the running world is fully depends of computer technique which play vital role in living style as well as working life from here and there. Today’s databases and data repositories contain so much data and ...
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CODE CLONE DETECTION USING WEIGHTED FEED FORWARD BACK PROPOGATION NEURAL NETWORK
... and Neural Network following various parameters that are FAR, FRR and ...with Neural Network that has more iterations and a training model that helps neural networks for better ...
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A neural network for counter terrorism
... forward back-propagation neural networks were developed using the JOONE toolset [8] which is an object based neural network framework with a graphical user ...The neural network ...
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Combined Sewer Overflow forecasting with Feed-forward Back-propagation Artificial Neural Network
... The first three years’ data were used for training the ANN while the remaining seven were used for testing. Of the various stopping criteria [18], termination following a fixed number of training iterations was adopted ...
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Diabetic prediction using fuzzy back propagation and analysis
... artificial neural network and fuzzy back propagation using triangular membership function and naive Bayesian are all implemented in JDK ...In fuzzy back propagation results have ...
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Comparative Study of different methods used for GPS GDOP Approximation
... but neural network method have a good accuracy compared to remaining two ...a neural network model is superior when compared to matrix inversion or closed loop algorithm while filtering the ...
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Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation
... type-2 fuzzy logic. In order to teach type-2 fuzzy logic, the gradient descent method and the neuro-fuzzy-genetic hybrid approach have been ...
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