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Content Based Image Retrieval using Novel Gaussian Fuzzy Feed Forward-Neural Network

2011 Journal of Computer Science  
We proposed to extract features from an image using Discrete Cosine Transform, extract relevant features using information gain and Gaussian Fuzzy Feed Forward Neural Network algorithm for classification  ...  Content-Based Image Retrieval (CBIR) system responds to image queries as input and relies on image content, using techniques from computer vision and image processing to interpret and understand it, while  ...  The Gaussian Fuzzy Feed Forward Neural Network (GFFF-NN) proposed in this study uses the criteria specified in Table 1 .  ... 
doi:10.3844/jcssp.2011.958.961 fatcat:dqydeqthargtvblueftx5yk3ai

Solving Uncertain Problems using ANFIS

Dr G.S.V.P. Raju, V. Mary Sumalatha, K.V. Ramani, K.V. Lakshmi
2011 International Journal of Computer Applications  
The purpose of the paper is to solve an engineering problem, power failures in personal computers using neuro fuzzy modeling system ANFIS.  ...  for the solution of machine learning problems lead to high machine intelligence quotient.  ...  Neural network architectures are classified into feed forward networks and feed backward networks like single layer feed forward networks, multi layer feed forward networks, recurrent networks, black propagation  ... 
doi:10.5120/3690-5152 fatcat:ktdioeb2ozdebhswpybq443h2u

An Intelligent Call Admission Control Decision Mechanism for Wireless Networks [article]

Ramesh Babu H.S., Gowrishankar, Satyanarayana P.S
2010 arXiv   pre-print
of the neural networks .The model is based on Recurrent Radial Basis Function Networks (RRBFN) which have better learning and adaptability that can be used to develop the intelligent system to handle  ...  This paper proposes a fuzzy neural approach for call admission control in a multi class traffic based Next Generation Wireless Networks (NGWN).  ...  This research work particularly use the feed forward neural networks which has the ability to map any nonlinear and non-stationary function to an arbitrary degree of accuracy [24] .One such popular feed-forward  ... 
arXiv:1004.4444v1 fatcat:67lkeq2zqfbx7mz3ysuonhsbz4

A QoS Provisioning Recurrent Neural Network based Call Admission Control for beyond 3G Networks [article]

Ramesh Babu H.S., Gowrishankar, Satyanarayana P.S
2010 arXiv   pre-print
This paper proposes a fuzzy neural approach for making the call admission control decision in multi class traffic based Next Generation Wireless Networks (NGWN).  ...  The model is based on recurrent radial basis function networks which have better learning and adaptability that can be used to develop intelligent system to handle the incoming traffic in an heterogeneous  ...  This research work particularly uses the feed forward neural networks which has the ability to map any nonlinear and non-stationary function to an arbitrary degree of accuracy [24] .One such popular feed-forward  ... 
arXiv:1004.3563v1 fatcat:7dtpo3anfbgl3hokcqrvtxj4p4

A novel method for face recognition using neural networks with optical and infrared images

D. Surya, R. Krishnaveni
2015 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)  
To recognize the user based on Neural Network done by Feed Forward Neural Network and Fuzzy Neural Network with Optical and Infrared image.  ...  To identify the correct person to improve the detection rate and also to reduce the time complexity.  ...  presented a Neural Network (NN) based face recognition system by using Feed Forward Neural Network (FFNN) and Fuzzy Neural Network, the images has been tested for the performance.  ... 
doi:10.1109/iciiecs.2015.7192998 fatcat:on3i6nl455ebjftzddufk5vcwe

Design of Synthetic Optimizing Neuro Fuzzy Temperature Controller for Dual Screw Profile Plastic Extruder Using Labview

2011 Journal of Computer Science  
In order to conquer this problem the system is premeditated with neuro fuzzy controller using LabVIEW.  ...  Approach: The existing technique involved is conventional PID controller, Neural controller, mamdani type Fuzzy Logic Controller and the proposed method is neuro fuzzy controller.  ...  For a SISO system, only one input line and one output line are required for the Elman network that is simpler than the tapped delay line method commonly adopted when feed forward nets are employed for  ... 
doi:10.3844/jcssp.2011.671.677 fatcat:v4uet7dj65cgdg25uwodyfwvue

Classification of Cervical Cancer Using Artificial Neural Networks

M. Anousouya Devi, S. Ravi, J. Vaishnavi, S. Punitha
2016 Procedia Computer Science  
Artificial neural network (ANN) plays an important role in many medical imaging applications.  ...  The classification between the normal, abnormal and cancerous cells is identified by using an artificial neural network which produces accurate results than the manual screening methods like Pap smear  ...  The feed forward neural network with fuzzy for classification is unclear.  ... 
doi:10.1016/j.procs.2016.06.105 fatcat:h7kpnhqtufhtbave3hhmpq7cmy

Advanced Time Series Forecasting Methods [chapter]

Cagdas Hakan Aladag, Erol Eǧrioǧlu
2012 Advances in Time Series Forecasting  
There are various types of artificial neural networks. One of them is called as feed forward neural networks. The feed forward neural networks have been used successfully in many studies [10] .  ...  Determining Fuzzy Relations To establish fuzzy relationships, feed forward neural networks are used.  ...  E Elman's recurrent neural network 7, [11] [12] [13] [14] [15] 17, 85, 87, 88.  ... 
doi:10.2174/978160805373511201010003 fatcat:zosgo2jh2rhw7mnu2r4ctm3w74

Fuzzy Neural Network Models for Supervised Classification: Multispectral Image Analysis

Arun D. Kulkarni, Kamlesh Lulla
1999 Geocarto International  
Neural networks provide algorithms for learning, classification, and optimization whereas fuzzy logic deals with issues such as reasoning on a higher (semantic or linguistic) level.  ...  It has been well established that neural networks provide a reasonable and powerful alternative to conventional classifiers.  ...  A feed-forward neural network also maps multiple inputs to multiple outputs. Therefore it is always possible to implement an inference engine with a feed-forward neural network.  ... 
doi:10.1080/10106049908542127 fatcat:nx4xfn35mrgadic4wobilp4yh4

Recurrent Fuzzy Neural Networks and Their Performance Analysis [chapter]

R.A. Aliev, B. Fazlollahi, B.G. Guirimov, R.R. Aliev
2008 Recurrent Neural Networks  
It should be noted that in [27] feedback links in the second layer only are added to the fuzzy feed-forward neural network.  ...  Levenberg-Marquardt algorithm with regularization is used for adjusting crisp weights and biases of the feed-forward and feed-back connections of the recurrent neuro-fuzzy network.  ...  The overview of the works on training methods for fuzzy feed-forward neural networks is given in [10] .  ... 
doi:10.5772/5540 fatcat:5wp65wewurdtpayowiwhcjj7rq

Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches

Mohammad A. M. Abushariah, Assal A. M. Alqudah, Omar Y. Adwan, Rana M. M. Yousef
2014 Journal of Software Engineering and Applications  
The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach  ...  Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively.  ...  Multilayer Perceptron (MLP) In this research, MLP is used as one neural network model since it follows feed-forward architecture and supervised training.  ... 
doi:10.4236/jsea.2014.712093 fatcat:v5pguvx3jfgqtlbf5qj35jqphq

Intrusion Detection System and Artificial Intelligent [chapter]

Khattab M.
2011 Intrusion Detection Systems  
briefly for feed-forward Neural Network (NN).  ...  In fact, in this work we used one type of neural network namely (feed-forward neural network), but with two different architectures, one trained with nonfuzzified dataset and other for the fuzzified data  ...  of the anomalies of user behaviors and many others.  ... 
doi:10.5772/15271 fatcat:4axho5p2ebhy5kwonoh74byrqi

Polymer electrolyte membrane fuel cell control with feed-forward and feedback strategy

Omar Rgab, DL Yu, JB Gomm
2011 International Journal of Engineering, Science and Technology  
The feed-forward control is achieved using different methods, including look-up table, fuzzy logic and neural network, to improve the fuel cell stack breathing control and prevent the problem of oxygen  ...  Firstly, the feed-forward controller is used to generate directly an input voltage of the compressor according to the current demand.  ...  After fine tuning, the PID controller that is used here with Feed-forward controllers for oxygen ratio regulation is s d K s i K p K s Wc + + = ) ( (2) Feed-forward fuzzy logic controller: The fuzzy  ... 
doi:10.4314/ijest.v2i10.64012 fatcat:oelwionhtbczhkaydkgkbxojym

Performance Optimisation of Learning Feed Forward Control

Wubbe J.R. Velthuis, Theo J.A. de Vries, Job van Amerongen
1997 IFAC Proceedings Volumes  
The type of neural network is a single layer network, in which B-spline basis functions are used to store the input-output mapping.  ...  The feed forward controller is implemented as a neural network that is trained during control in order to minimise the tracking error.  ...  C P + - + + y e r u t Q Network Neural Figure 1: Learning Feed Forward Control In case of repetitive paths the periodic time can be used as input of the feed forward controller.  ... 
doi:10.1016/s1474-6670(17)41346-2 fatcat:ltrpwoxkijf27dn4rnwcbrclaq

Modeling of Soil Cation Exchange Capacity Based on Fuzzy Table Look-up Scheme and Artificial Neural Network Approach

Ali Keshavarzi, Fereydoon Sarmadian, Reza Labbafi, Majid Rajabi Vandechali
2011 Modern Applied Science  
Then, neural network model (feed-forward back propagation network) and fuzzy table look-up scheme were employed to develop a pedotransfer function for predicting soil CEC using easily measurable characteristics  ...  In this study, a new approach is proposed as a modification to a standard fuzzy modeling method based on the table look-up scheme. 70 soil samples were collected from different horizons of 15 soil profiles  ...  On the other hand, in a recurrent network additional weighted connections are used to feed previous activations back into the network. The structure of a feed-forward ANN is shown in Figure 2 .  ... 
doi:10.5539/mas.v5n1p153 fatcat:4doafkfmabg37pob6johyxpfy4
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