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  • computation
  • Neural networks have been a subject of intense research activities over the past few decades due to their wide applications in many areas such as signal processing, pattern recognition, associative memories, parallel computation, and optimization solution. (hindawi.com)
  • 2018
  • Arana-Daniel N., Valdés-López J., Alanís A.Y., López-Franco C. (2018) Traversability Cost Identification of Dynamic Environments Using Recurrent High Order Neural Networks for Robot Navigation. (springer.com)
  • Theory
  • Concepts from linear system theory were adapted to represent some aspects of neural dynamics, including solutions of simultaneous linear equations \(Y = AX\) using matrix theory, and concepts about cross-correlation. (scholarpedia.org)
  • Rovithakis, G.A., Christodoulou, M.A.: Adaptive Control with Recurrent High-Order Neural Networks: Theory and Industrial Applications. (springer.com)
  • dynamics
  • Direction-of-Change Forecasting using a Volatility- Based Recurrent Neural Network ," CeNDEF Working Papers 06-16, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. (repec.org)
  • Although previous researchers have explored the potential of this network to solve combinatorial optimization problems or store reoccurring activity patterns as attractors of its deterministic dynamics, a basic open problem is to design a family of Hopfield networks with a number of noise-tolerant memories that grows exponentially with neural population size. (readbyqxmd.com)
  • interval
  • This paper is concerned with the robust dissipativity problem for interval recurrent neural networks (IRNNs) with general activation functions, and continuous time-varying delay, and infinity distributed time delay. (hindawi.com)
  • It is also possible that there is no equilibrium point in some situations, especially for interval recurrent neural networks with infinity distributed delays. (hindawi.com)
  • Identification
  • Recurrent High Order Neural Networks (RHONN) trained with Extended Kalman Filter (EKF) are used to identify rough terrain traversability costs, and besides the good results in the identification tasks, we get the advantages of using a robust machine learning method such as RHONNs. (springer.com)
  • spatial
  • Anderson (1968) initially described his intuitions about neural pattern recognition using a spatial cross-correlation function. (scholarpedia.org)
  • However, neural networks usually have a spatial extent due to the presence of a multitude of parallel pathways with a variety of axon sizes and lengths. (hindawi.com)
  • Memory
  • Robust Exponential Memory in Hopfield Networks. (readbyqxmd.com)
  • Previous studies have shown that there are many common structures between the neural network of pain and memory, and the main structure in the pain network is also part of the memory network. (readbyqxmd.com)
  • networks that's called Long Short Term Memory. (coursera.org)
  • You can consider the dynamic state of a neural network to be a short term memory. (coursera.org)
  • the rest of the recurrent neural network gets written into the memory cell. (coursera.org)
  • Feedback networks structured to have memory and a notion of "current" and "past" states, which can encode time (or whatever). (danmackinlay.name)
  • A memory cell is composed of four main elements: an input gate, a neuron with a self-recurrent connection (a connection to itself), a forget gate and an output gate. (danmackinlay.name)
  • Research shows them to be one of the most powerful and useful type of neural network, alongside the attention mechanism and memory networks . (skymind.ai)
  • Since recurrent networks possess a certain type of memory, and memory is also part of the human condition, we'll make repeated analogies to memory in the brain. (skymind.ai)
  • It is often said that recurrent networks have memory. (skymind.ai)
  • As a result, the network actually learns a memory set which is "near-orthogonal", even though the visible components of the memories are randomly selected. (ubc.ca)
  • It is also shown that the memory recovery process developed for the new network can be used to greatly expand the radius of attraction of standard Hopfield networks for "incomplete" (as opposed to "noisy") prompts. (ubc.ca)
  • The mathematical analysis begins by deriving an expression for the free energy of a Hopfield network when a near-orthogonal memory set has been stored. (ubc.ca)
  • Over decades such neuronal feedback attracted a huge amount of theoretical modeling [ 1 - 3 ] and one of the most prominent functions that is proposed for the recurrent synaptic connections is that of associative memory. (springeropen.com)
  • connectivity
  • The aim of the present analyses was to investigate whether emotionally challenged remitted depressed participants show higher respiration pattern variability (RPV) and whether this is related to mood, clinical outcome and increased default mode network connectivity. (readbyqxmd.com)
  • Many brain areas exhibit extensive recurrent connectivity. (springeropen.com)
  • continuous
  • Continuous-nonlinear network laws typically arose from an analysis of behavioral or neural data. (scholarpedia.org)
  • Decoding continuous hind limb joint angles from sensory recordings of neural system provides a feedback for closed-loop control of hind limb movement using functional electrical stimulation. (readbyqxmd.com)
  • delays
  • Therefore, increasing attention has been paid to the problem of stability analysis of neural networks with time-varying delays, and recently a lot of research works have been reported for delayed neural networks and system (see [ 1 - 17 ] and references therein). (hindawi.com)
  • It is also shown that memristor-based recurrent neural networks with time-varying delays are stabilizable at the origin of the state space by using a linear state feedback control law with appropriate gains. (edu.au)
  • stability
  • Therefore, many initial findings on the global dissipativity [ 18 , 22 - 30 ] or Lagrange stability [ 31 - 36 ] analysis of neural networks have been reported. (hindawi.com)
  • time
  • That is, a feedforward network has no notion of order in time, and the only input it considers is the current example it has been exposed to. (skymind.ai)
  • Recurrent networks, on the other hand, take as their input not just the current input example they see, but also what they have perceived previously in time. (skymind.ai)
  • memories
  • Finally, the theoretical maximum information content of sets of near-orthogonal memories is calculated as a function of the level of orthogonality, and is compared to the amount of information that can be stored in the new network. (ubc.ca)
  • approach
  • However, from a practical point of view, it is not always the case that the neural network trajectories will approach a single equilibrium point that is the equilibrium point will be unstable. (hindawi.com)
  • deep
  • A knowledge graph is traversed by receiving a knowledge graph at a deep neural network, the knowledge graph including a plurality of nodes connected by a. (patents.com)