high-order neural networks
Anti periodic solution for impulsive high order Hopfield neural networks with time varying delays in the leakage terms
... [–]. High-order neural networks have been the object of intensive anal- ysis by numerous authors since high-order neural networks have stronger approximation ...
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Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
... tained by training with various configurations that fit in memory for 24 hours each on GPU (cf. Fig 5) were 10.13 and 9.37, respectively, which is no bet- ter than SVM bow2. Since excellent performances were reported on ...
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Solving high-order partial differential equations with indirect radial basis function networks
... Neural networks have found application in many disciplines: neurosciences, math- ematics, statistics, physics, computer science and engineering ...function networks (RBFNs) to solve partial ...
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Application of Artificial Neural Networks in Order to Predict Mahabad River Discharge
... in order to determine appropriate values in flood, drought, drinking, agricultural and industral ...in order to train, validate and test, ...in order to determine Mahabad River ...different ...
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Modelling of Multi Layer Feed forward Neural Networks to Determine the Compressive Strength of Marmara Region Aggregate's Concrete
... the high quality concretes and showed in his studies that the compressive strength and slump values would be able to be estimated by ANN ...and neural results of concrete compression strength from 24 to 105 ...
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Blockchain based Smart P2P Lending using Neural Networks
... ensemble neural network could be trained on a selected feature set in order to predict credit ...in order to enhance the accuracy of such a ...gain high return on investment and the borrowers ...
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Second Order Semantic Dependency Parsing with End to End Neural Networks
... capturing high-order information to some ...of high-order parts, it may require more training data to learn this capability than a high-order ...utilizing high- ...
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Anti periodic solutions for high order cellular neural networks with mixed delays and impulses
... decades, high-order cellular neural networks (HCNNs) have been exten- sively investigated due to their immense potential of application perspective in various fields such as signal and image ...
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Introduction to neural networks in high energy physics
... cient order will always be able to pass through all the data points and thus produce smaller ...of neural networks, a behavior like this is called overtraining, and we have already encountered it in ...
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Artificial Intelligence Applied to Project Success: A Literature Review
... with neural networks in order to get a mapping between managerial elements of project management and project management ...between neural networks and regression analysis tools for ...
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Generate Faces Using Ladder Variational Autoencoder with Maximum Mean Discrepancy (MMD)
... Generative Models have been shown to be extremely useful in learning fea- tures from unlabeled data. In particular, variational autoencoders are capable of modeling highly complex natural distributions such as images, ...
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Existence and Stability of Periodic Solution in Impulsive Hopfield Networks with Time-Varying Delays
... According to Theorem 1, impulsive Hopfield neural networks Eq. (9) has a unique 1-periodic solution which is globally asymptotically stable(see Figs.1-Figs.4). In order to clearly observe the change ...
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THE ROLE OF INFORMATION TECHNOLOGY ON THE GROWTH OF FIRMS: A VALUE ADDED ONSIDERATION
... Classification of web pages based on their contents is useful to the search engines to give appropriate data to the user. In this work, features are extracted from the ontology representation of the content available in ...
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Deep Neural Networks Constrained by Decision Rules
... of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make decisions by selecting ...
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Pseudo almost periodic solutions for shunting inhibitory cellular neural networks with continuously distributed delays
... cellular neural networks (SICNNs) was introduced by Bouzerd- out and Pinter [] in ...the networks along with stability analysis of their ...of networks is of the greatest ...of neural ...
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Deep Learning as a Frontier of Machine Learning: A Review
... artificial neural networks each has a specific property and can be applied in a different problem ...artificial neural networks have been used very popularly in many ...feedback neural ...
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Chaotic Time Series Forecasting Using Higher Order Neural Networks
... During the simulations, we noticed that increasing network order of PSNN results in decreasing forecasting performance on Sunspot time series but it helps PSNN on Mackey-Glass time series. Learning curves for the ...
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Application of Higher Order Neural Networks to Financial Time Series Prediction
... Some examples of the former are Auto Regression (AR), ARCH, Box-Jenkins (Box & Jenkins, 1976), and Kalman Filter (Harvey, 1989). Some examples of the latter are ANNs (Zhang, Patuwo, & Hu, 1998), Fuzzy Logic and ...
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Neuroplasticity, neural reuse, and the language module
... Perhaps we might first note that when identifying a single psychological state to establish the necessary conditions for MR, nothing Bechtel and Mundale say actually precludes the choice to go abstract. If context is ...
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Second Order Learning Algorithm for Back Propagation Neural Networks
... The proposed approach was validated on a standard feedforward neural network with one hidden layer by having five hidden nodes. The training data set was created by using the function y = x + sin( 2 * π * x ), ...
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