Neural Network Model
A Recurrent Neural Network Model for Solving Linear Semidefinite Programming
... Abstract. In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and syn- thesis in systems and ...
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A Neural Network Model to Solve DEA Problems
... Artificial Neural Network ...with neural network model, provides a promising rich approach to optimal ...new neural network model is used to estimate the ...
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Perceptive Functions and Memory in Neural Network Model
... Hebbian network. A neural network model of associative memory in a small region of the human brain unconventionally depends, on dis-inhibitation of links between excitation neurons instead of ...
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Towards Neural Network Model for Insulin/Glucose in Diabetics-II
... general neural network model that resembles the interactions between glucose concentration levels and amount of insulin injected in the bodies of ...our model. Two types of neural ...
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A paired neural network model for tourist arrival forecasting
... paired neural network ...autoregressive neural network models as a parallel structure. The proposed model is evaluated by the tourist arrival data to United States from twelve source ...
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A Concise Neural Network Model for Estimating Software Effort
... estimation model based on artificial neural networks. The model is designed accordingly to improve the performance of the network that suits to the COCOMO ...forward neural ...
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An Artificial Neural Network Model to Forecast Exchange Rates
... adaptive neural network (ADNN) with the adaptive metrics of inputs and a new mechanism for ad- mixture of outputs is proposed for time-series ...proposed model outperforms the auto-re- gression (AR), ...
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Artificial Neural Network Model for Predicting Lung Cancer Survival
... the model while the remaining set is used for the ...Other model selection criteria like Akaike Information Criterion and Bayesian Information Criterion are not suitable for model selection as the ...
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Predicting stock price based on BP neural network model
... BP neural network is used to train and predict the stock ...prediction model is small, the calculated average error is only ...BP neural network is ex- ...
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Application of artificial neural network model in diagnosis of Alzheimer’s disease
... Methods: A population based nested case-control study design was used. 89 new AD cases with good compliance who were willing to provide urine and blood specimen were selected from the cohort of 2482 community- dwelling ...
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The Inside Outside Recursive Neural Network model for Dependency Parsing
... bottom-up like the latter but also top-down. Elman (1990) invented the simple recurrent neural network (SRNN) architecture which is ca- pable of capturing very long histories. Mikolov et al. (2010) then ...
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Mechanistic Model versus Artificial Neural Network Model of a Single Cell PEMFC
... The PEMFC system is a nonlinear, multivariable electrochemical system that is hard to model. Describing this system requires the understanding of complex internal phenomena at molecular level. A large number of ...
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A Convolutional Neural Network Model Robust To Distorted Fingerprints
... Artificial neural networks are better but still do not well represent images, because image pixels in an image are vectorised prior to training an ANN or using an ANN for recognition, thereby deforming the pixels’ ...
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Classification of neovascularization using convolutional neural network model
... Convolutional Neural Network (CNN) model and classification method such as Support Vector Machine, k-Nearest Neighbor, Naïve Bayes classifier, Discriminant Analysis, and Decision ...
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Bifurcation and Chaos in Delayed Cellular Neural Network Model
... periodic orbits that a chaotic attractor encompasses. Furthermore, different from the work of Yang and Huang [14] where adjustable parameter lies off the main diagonal (self-connection weights), we consider it on the ...
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An artificial neural network model for rainfall forecasting in Bangkok, Thailand
... artificial neural network (ANN) is an interconnected group of artificial neurons that has a natural property for stor- ing experiential knowledge and making it available for ...feedforward neural ...
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A Unified Neural Network Model for Geolocating Twitter Users
... Previous network-based methods (Jurgens, 2013; Compton et ...user network component. One main reason is that our model is less sophisticated (but more scalable) comparing to the mentioned ...
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Image Watermarking by DWT and Neural Network Model
... of data was done at receiver side so trained neural network was send at extraction side. Hence security of the data increases as intruder should know embedded image and trained neural network ...
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A Hybrid Neural Network Model for Commonsense Reasoning
... The second category of models are semantic similarity models. Wang et al. (2019); Opitz and Frank (2018) formulated WSC and PDP as a se- mantic matching problem, and proposed to use two variations of the Deep Structured ...
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Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model
... the network at a low cost without professional knowledge ...malicious network attacks. Therefore, network intrusion detection is getting more and more attention with the development of ...to ...
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