higher order neural networks
Chaotic Time Series Forecasting Using Higher Order Neural Networks
... of higher order neural networks (HONNs) to forecast benchmark chaotic time ...link neural network (FLNN) and pi-sigma neural network ...and neural networks ...
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Production Forecasting of Petroleum Reservoir applying Higher Order Neural Networks (HONN) with Limited Reservoir Data
... From the case studies, the performance evaluation criteria indicates that the better oil production forecasting can be achieved using HONN with LSO with only one input parameter i.e. oil production data. In this study, ...
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Application of Higher Order Neural Networks to Financial Time Series Prediction
... It was mentioned previously that PHONN Model#3 comprises groups of PHONN#2 neurons. When applied to financial time-series prediction, PHONN groups produce up to an order of magnitude performance improvement over ...
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A functional link neural network with modified cuckoo search for prediction tasks
... of Neural Network may be declared via its structure, which is signified using the network architecture and t h e design of relations among the nodes, its technique of defining the joining weights, and the ...
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Second Order Semantic Dependency Parsing with End to End Neural Networks
... powerful neural network for semantic dependency parsing using a bilinear or biaffine (Dozat and Manning, 2016) layer to encode the interaction between ...encode higher-order parts with hand-crafted ...
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Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
... Convolutional neural network (CNN) is a neu- ral network that can make use of the inter- nal structure of data such as the 2D structure of image ...word order) of text data for accurate ...
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Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
... We presented a theoretical investigation of GNNs, showing that a wide class of GNN architectures cannot be stronger than the 1-WL. On the positive side, we showed that, in principle, GNNs possess the same power in terms ...
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First-order logic learning in artificial neural networks
... In PAN, a learned rule is distributed in the network; for example, the rule P (X, Y ) ∧ P (Y, Z ) ⇒ P (X, Z) is represented in the network as P (X, Y ) ∧ P (W, Z) ∧ (Y = W ) ∧ (U = X ) ∧ (V = Z) ⇒ P(U, V ) and P (X, ...
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Adaptive higher order neural network models and their applications in business
... ward Neural Network (FNN) was able to adapt its activation function by varying the control points of a Catmull-Rom cubic ...in networks with traditional fixed neuron activation functions such as the sigmoid ...
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Application of Artificial Neural Networks in Order to Predict Mahabad River Discharge
... artificial neural net- work model based on artificial intelligence is widely used in various fields of engineering, in particular, water and river ...artificial neural network and regression in a study on ...
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THE ROLE OF INFORMATION TECHNOLOGY ON THE GROWTH OF FIRMS: A VALUE ADDED ONSIDERATION
... J. Alamelu Mangai et al presented A Novel Feature Selection Framework for Automatic Web Page Classification [13]. Most of the classification algorithm’s performance depended on the elimination of the noisy and outlier ...
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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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Second Order Learning Algorithm for Back Propagation Neural Networks
... The parity problem is also one of the classical and considers the most popular initial testing tasks that are very demanding for classification particularly for the neural network to solve. This is because the ...
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Higher Order Risk Measure and (Higher Order) Stochastic Dominance
... In sum, we find that the preference of second-order stochastic dominance implies the preference of the corresponding Omega ratios and the preference of third-order stochastic dominance implies the ...
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Neural Units with Higher Order Synaptic Operations for Robotic Image Processing Applications
... Neural units with higher-order synaptic operations for robotic image processing applications 227 CCD camera image acquisition were processing & edge detection Ultrasonic, infrared, odome[r] ...
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Blockchain based Smart P2P Lending using Neural Networks
... computationally infeasible to alter any part of the Blockchain. Blockchain-based platforms have also become a popular way to raise capital. Startups have started using Initial Coin Offerings (ICOs) in order to ...
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Deep Learning as a Frontier of Machine Learning: A Review
... belief networks are the example of deep learning model which are applied to such unsupervised ...Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Deep Belief Network ...
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Recognizing Handwritten Alphabets using Neural Networks
... Neural Networks are a programming archetype which is inspired by the biological functioning of neurons. They learn by learning from data which is usually observational in nature. Since it is inspired by ...
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Recent trends in neuromorphic engineering
... PuDianNao [27] by Liu et al. is a neuromorphic accelerator which can run seven machine learning algorithms, viz. k-means, k-nearest neighbors, naive bayes, support vec- tor machines, linear regression, classification ...
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Data Mining using Neural Networks
... solving a problem through simulated annealing will prove incompatible with that of virtual machines or we can say that while working with virtualization of machines it will be quite incompatible with that of the features ...
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