FPGA implementation of multilayer feed forward neural network architecture using VHDL

2012 ◽  
Author(s):  
S. Hariprasath ◽  
T. N. Prabakar
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fpga Implementation ◽  
Neural Network Architecture ◽  
Feed Forward Neural Network ◽  
Feed Forward

A HYBRID NEURAL NETWORK ARCHITECTURE FOR THE CLASSIFICATION OF THE HADRONIC DECAYS OF THE Z0

1993 ◽  
Vol 04 (05) ◽  
pp. 977-981
Author(s):  
G. COSMO ◽  
A. DE ANGELIS
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
High Energy Physics ◽  
Substantial Reduction ◽  
High Energy ◽  
Hadronic Decays ◽  
Neural Network Architecture ◽  
Feed Forward Neural Network ◽  
Feed Forward

Feed-Forward Neural Networks have shown to be a useful tool for the automatic classification of events in High Energy Physics. A shortcoming of the method is anyway given by the large value of simulated events to be used for training the classifier. In this paper, we describe an alternative Neural Network architecture that allows a substantial reduction of the CPU time spent in the training phase. This architecture has been tested on a complex problem, such as the classification of the hadronic decays of the Z0, and its performance has been compared with that of a Feed-Forward Neural Network.

scholarly journals Application of Feed Forward Neural Network for Prediction of Optimum Coagulant Dose in Water Treatment Plant

2019 ◽  
Vol 8 (12) ◽  
pp. 1853-1856
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Network Architecture ◽  
Assessment Tool ◽  
Research Work ◽  
Treatment Plant ◽  
Training Algorithm ◽  
Feed Forward Neural Network ◽  
Plant Laboratory ◽  
Feed Forward

Coagulation is anecessary process used mainly to reduce turbidity and natural organic matter in water treatment. The dosage of coagulant required is conventionally determined by carrying out jar tests which consume time and chemicals.In India, coagulant dose in a WTP remains constant during certain periods due to delay in jar testing, which may lead to under-dosing or over-dosing of coagulant. This research work is focused on applying artificial neural network (ANN) approach to predict coagulant dose in a WTP. Forty-eight months daily water testing data concerning inlet & outlet water turbidity and coagulant dose were obtained from the plant laboratory for ANN modelling. The appropriate architecture of feed forward neural network (FFNN) coagulant models were established with several steps of training and testing by applying various training algorithms vizLevenberg-Marquardt (LM) and Bayesian regularization (BR), resilient back propagation (RBP), one step secant(OSS),variants of conjugate gradient(CG) and modifications of gradient descent (GD) with evaluating coefficient of correlation (R) & mean square error (MSE). Further, best performed LM and BR training algorithm were used for development of four ANN models of FFNN for prediction of coagulant dose at WTP. FFNN coagulant model with BR training algorithm provided excellent estimates with network architecture (2-50-1) for coagulant dose with maximum value of R= 0.943 (training) and R = 0.945 (testing). Thus, ANN provided an effective diagnosing tool to understand the non-linear behavior of the coagulation process, and can be used as a valuable performance assessment tool for plant operators and decision makers.

A SIMPLE NEURAL NETWORK APPROACH TO INVARIANT IMAGE RECOGNITION

2001 ◽  
Vol 15 (01) ◽  
pp. 11-17
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Distribution Function ◽  
Image Recognition ◽  
Network Architecture ◽  
Cellular Neural Network ◽  
Angle Distribution ◽  
Neural Network Architecture ◽  
Feed Forward Neural Network ◽  
Neural Network Approach ◽  
Simple Neural Network

In this paper we propose a simple neural network architecture for invariant image recognition. The proposed neural network architecture contains three specialized modules. The neurons from the first module are connected in a cellular neural network structure, which is responsible for image processing: edge detection and segmentation. The second module is a feed forward neural network for invariant feature extraction from the sensorial layer: computation of the pair distribution function and bond angle distribution function. The third module is responsible for image classification. An application to the face recognition problem is also presented.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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