Back-Propagation Neural Network Architecture for Solving the Double Dummy Bridge Problem in Contract Bridge

2014 ◽  
Author(s):  
M. Dharmalingam ◽  
R. Amalraj
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Architecture ◽  
Bridge Problem ◽  
Contract Bridge

scholarly journals ON SOME BOUNDS OF THE MINIMUM EDGE DOMINATING ENERGY OF A GRAPH

2021 ◽  
Vol 12 (4) ◽  
pp. 1285-1296
Author(s):  
Dr. Gauri Ghule , Et. al.
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fuzzy Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Fuzzy Classifier ◽  
Manual Intervention ◽  
The Neural Network ◽  
Hidden Neurons

Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.

Prediction of Yarn Quality Based on BP Neural Network

2011 ◽  
Vol 331 ◽  
pp. 449-453
Author(s):  
Jing Yuan ◽  
Ying Lin Li ◽  
Su Ying Chen
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Network Architecture ◽  
Raw Materials ◽  
Nonlinear Approximation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Yarn Quality ◽  
Spinning Process ◽  
Hidden Layer

As the quality of yarn and the fiber indicators are nonlinear relationship, the traditional mathematical models or empirical formula has been unable to accurately resolve the problem. In view of artificial neural networks do not need to build accurate mathematical models, applicable to solving the problem of yarn quality prediction. In this paper, good nonlinear approximation ability of BP (Back Propagation) neural network be used, the use of neural network toolbox of MATLAB functions for modeling, good results was obtained. Prediction model set a hidden layer, using three-tier network architecture, and take the input layer 4 nodes, hidden layer 8 nodes and output layer 2 nodes. According to forecast results, can ensure the yarn quality effectively, use of raw materials rationally, to achieve optimal distribution of cotton. Meanwhile, the spinning process design can also be provided validation, for the development of new products to provide a theoretical basis.

scholarly journals Estimate Reliability Parameters in Bio_Fuel Plant Using Neural Network Architecture

2019 ◽  
Vol 8 (11) ◽  
pp. 440-445
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Approach ◽  
Reliability Parameters ◽  
Feed Forward Back Propagation ◽  
Estimate Reliability ◽  
Increasing Demand ◽  
Time Systems

The world’s ever increasing demand for energy and abating global warming, suitable renewable sources of energy are highly in demand. The wastes from industries such as plant’s biomass could meet the energy requirements. In this paper authors analyze bio fuel plant system which produces ethanol fuel. This system is divided into various subsystems considering multiple phases in the production of ethanol. The structure of this system consists of interconnected networks of components on very large dimensional scales escalates the complexity of systems that can increase the degradation of system's functioning. In view of this, one of the computational intelligence approach, neural network (NN), is useful in predicting various reliability parameters. To improve the accuracy and consistency of parameters, Feed Forward Back Propagation Neural Network (FFBPNN) is used. All types of failures and repairs follow exponential distributions. System state probabilities and other parameters are developed for the proposed model using neural network approach. Failures and repairs are treated as neural weights. Neural network's learning mechanism can modify the weights due to which these parameters yield optimal values. Numerical examples are included to demonstrate the results. The iterations are repeated till the convergence in the error tends up to 0.0001 precision using MATLAB code. The reliability and cost analysis of the system can help operational managers in taking the decision to implement it in the real time systems

scholarly journals CFNN: Correlation Filter Neural Network for Visual Object Tracking

2017 ◽  
Author(s):  
Yang Li ◽  
Zhan Xu ◽  
Jianke Zhu
Keyword(s):  
Neural Network ◽  
Visual Tracking ◽  
Network Architecture ◽  
Back Propagation ◽  
Correlation Filter ◽  
Visual Object ◽  
Neural Network Architecture ◽  
Visual Object Tracking ◽  
Single Target ◽  
Wide Range

Albeit convolutional neural network (CNN) has shown promising capacity in many computer vision tasks, applying it to visual tracking is yet far from solved. Existing methods either employ a large external dataset to undertake exhaustive pre-training or suffer from less satisfactory results in terms of accuracy and robustness. To track single target in a wide range of videos, we present a novel Correlation Filter Neural Network architecture, as well as a complete visual tracking pipeline, The proposed approach is a special case of CNN, whose initialization does not need any pre-training on the external dataset. The initialization of network enjoys the merits of cyclic sampling to achieve the appealing discriminative capability, while the network updating scheme adopts advantages from back-propagation in order to capture new appearance variations. The tracking pipeline integrates both aspects well by making them complementary to each other. We validate our tracker on OTB-2013 benchmark. The proposed tracker obtains the promising results compared to most of existing representative trackers.

scholarly journals A Back Propagation-Type Neural Network Architecture for Solving the Complete n × n Nonlinear Algebraic System of Equations

2016 ◽  
Vol 06 (06) ◽  
pp. 455-480 ◽  
Author(s):  
Konstantinos Goulianas ◽  
Athanasios Margaris ◽  
Ioannis Refanidis ◽  
Konstantinos Diamantaras ◽  
Theofilos Papadimitriou
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Algebraic System ◽  
Back Propagation ◽  
System Of Equations ◽  
Neural Network Architecture ◽  
Nonlinear Algebraic System

Artificial Neural Network Architectures for Solving the Contract Bridge

2020 ◽  
pp. 44-52
Author(s):  
Dharmalingam M
Keyword(s):  
Neural Network ◽  
Imperfect Information ◽  
Back Propagation ◽  
Intermediate Stage ◽  
Elman Neural Network ◽  
Point Count ◽  
The Neural Network ◽  
Bridge Problem ◽  
Artificial Neural ◽  
Contract Bridge

Contract Bridge is an intelligent game, which enhances the creativity with multiple skills and quest to acquire the intricacies of the game, because no player knows exactly what moves other players are capable of during their turn. The Bridge being a game of imperfect information is to be equally well defined, since the outcome at any intermediate stage is purely based on the decision made on the immediate preceding stage. One among the architectures of Artificial Neural Networks (ANN) is applied by training on sample deals and used to estimate the number of tricks to be taken by one pair of bridge players is the key idea behind Double Dummy Bridge Problem (DDBP) implemented with the neural network paradigm. This study mainly focuses on Cascade-Correlation Neural Network (CCNN) and Elman Neural Network (ENN) which is used to solve the Bridge problem by using Resilient Back-Propagation (R-prop) Algorithm and Work Point Count System.

Does Artificial Neural Network Forecast Better for Excessively Volatile Currency Pairs?

2017 ◽  
Vol 10 (2) ◽  
pp. 47-61
Author(s):  
Sarveshwar Kumar Inani ◽  
Manas Tripathi ◽  
Saurabh Kumar
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Network Architecture ◽  
Back Propagation ◽  
Neural Network Architecture ◽  
Forecast Horizon ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Daily Data ◽  
The Neural Network

This study predicts the exchange rates for three currency pairs (USD-INR, GBP-INR, and EUR-INR). We have used multi-layer perceptron (MLP) neural network architecture based on feed-forward with back-propagation learning method.  The sample of the study covers daily data for the period from January 2009 to January 2016. The findings of the study confirm that the neural network predicts better for more volatile currency pairs (GBP-INR and EUR-INR) as compared to a less volatile currency pair (USD-INR). The study further observes that the optimal forecast horizon for the neural network model should be equal to the optimal lag length used in the construction of the model. This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays a crucial role in the macro-economy of a country. Hence, prediction of currency exchange rate becomes imperative for various stakeholders such as government, the central bank, and investors to maximize the returns and minimize the risk in their decision-making.

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