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  • file
  • The point is that if the person setting up the training file does not fully understand the rule, then they can completely mess up the file and ruin any chance of the network working. (codeproject.com)
  • 2000
  • Jalali-Heravi, M, Parastar F (2000) Use of artificial neural networks in a QSAR study of anti-HIV activity for a large group of HEPT derivatives. (springer.com)
  • There's no description for self-reflectiveness and self-modification abilities into the initial description of semantic networks [Dudar Z.V., Shuklin D.E., 2000]. (wikipedia.org)
  • Ugur Halici "Reinforcement learning with internal expectation for the random neural network", European Journal of Operational Research 126 (2): 288-307, 2000. (wikipedia.org)
  • Aristidis Likas, Andreas Stafylopatis "Training the random neural network using quasi-Newton methods", European Journal of Operational Research 126 (2): 331-339, 2000. (wikipedia.org)
  • prediction
  • Due to the random nature of some steps in the following approach, numeric results might be slightly different every time the network is trained or a prediction is simulated. (mathworks.com)
  • A recursive neural network (RNN) is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order. (wikipedia.org)
  • behavior
  • An experiment to see if it possible to duplicate the behavior of the Adaline Network using Fuzzy Logic. (codeproject.com)
  • The primary purpose of this type of software is, through simulation, to gain a better understanding of the behavior the and properties of neural networks. (wikipedia.org)
  • weights
  • For this, the network calculates the derivative of the error function with respect to the network weights, and changes the weights such that the error decreases (thus going downhill on the surface of the error function). (wikipedia.org)
  • A recursive neural network is created by applying the same set of weights recursively over a differentiable graph-like structure by traversing the structure in topological order. (wikipedia.org)
  • They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. (wikipedia.org)
  • The neocognitron does not require units located at multiple network positions to have the same trainable weights. (wikipedia.org)
  • models
  • One of the most prominent models of intelligent agents built in computer memory is represented by neural networks (NN). (codeproject.com)
  • Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. (wikipedia.org)
  • There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models (sometimes also vice versa), and the other one searching for potential quantum effects in the brain. (wikipedia.org)
  • These simple models accounted for neural summation (i.e., potentials at the post-synaptic membrane will summate in the cell body). (wikipedia.org)
  • The further development of semantic neural network models. (wikipedia.org)
  • Play media Play media Spiking neural networks (SNNs) fall into the third generation of neural network models, increasing the level of realism in a neural simulation. (wikipedia.org)
  • Some large scale neural network models have been designed that take advantage of the pulse coding found in spiking neural networks, these networks mostly rely on the principles of reservoir computing. (wikipedia.org)
  • However, the real world application of large scale spiking neural networks has been limited because the increased computational costs associated with simulating realistic neural models have not been justified by commensurate benefits in computational power. (wikipedia.org)
  • This type of application software usually supports the simulation of complex neural models with a high level of detail and accuracy. (wikipedia.org)
  • learns
  • The intuition on why this work is that the neural network collapses into fewer layers in the initial phase, which makes it easier to learn, and thus gradually expands the layers as it learns more of the feature space. (wikipedia.org)
  • photonic
  • Properties that might be desirable in photonic materials for optical neural networks include the ability to change their efficiency of transmitting light, based on the intensity of incoming light. (wikipedia.org)
  • 1997
  • Hopke PK, Song X (1997) Source apportionment of soil samples by the combination of two neural networks based on computer. (springer.com)
  • Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1997 and set accuracy records in multiple applications domains. (wikipedia.org)
  • Fuzzy Logic
  • Lately I've been playing around with Fuzzy Logic amongst other things and a constant nagging thought has been lurking at the back of my mind as to if Fuzzy Logic could do the stuff that a Neural Network could do and would it be easier to develop, faster, more flexible? (codeproject.com)
  • graphs
  • RecCC is a constructive neural network approach to deal with tree domains with pioneering applications to chemistry and extension to directed acyclic graphs. (wikipedia.org)
  • Unlike
  • Unlike a single large network that can be assigned to arbitrary tasks, each module in a modular network must be assigned a specific task and connected to other modules in specific ways by a designer. (wikipedia.org)
  • Unlike the research simulators, data analysis simulators are intended for practical applications of artificial neural networks. (wikipedia.org)
  • Unlike the more general development environments data analysis simulators use a relatively simple static neural network that can be configured. (wikipedia.org)
  • explore
  • In this special issue, we wish to attract papers that deeply explore the role of brain-network organizations in different neurological disorders, which will promote studies of pathological mechanisms and developments of new therapeutic strategies. (hindawi.com)
  • A neural network without residual parts will explore more of the feature space, small perturbations will make it leave the manifold altogether, and thus needs it will need a lot more training data just to get it back on track. (wikipedia.org)