
	Where results make sense

Topic: Neural nets

Related Topics

Neural networks

Connectionism

Al Queda

Cognitive science

Psycholinguistics

Plow

Semo Sancus

Linguistics

Apostasy

White supremacist

1 E18 s

Fedora Core

SkyWest

Radagast

Time 100

	LTM - Why Use Neural Nets
		Neural nets, because they are pattern recognition tools, differ from other quantitative modeling tools such as deterministic models and statistical models.
		Neural nets begin training by assigning randow weights between nodes in the input layer and nodes in the hidden layer and between nodes in the hidden layer and the nodes of the output layer.
		For example, we have tested whether a neural net model that is built to simulate land use change in the Detroit area can be used to predict historical changes in the Twin Cities metropolitan area, and visa versa.
	ltm.agriculture.purdue.edu /neuralnets.htm (537 words)

	Neural network - Wikipedia, the free encyclopedia
		Neural network is sometimes used to refer to a branch of computational science that uses neural networks as models to either simulate or analyze complex phenomena and/or study the principles of operation of neural networks analytically.
		Neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain biological architecture is very much debated.
		In some of these systems neural networks, or parts of neural networks (such as artificial neurons) are used as components in larger systems that combine both adaptive and non-adaptive elements.
	en.wikipedia.org /wiki/Neural_nets (2911 words)

	Pulsed Neural Networks
		For instance, the timing of neural action potentials is now a recognized means for encoding information in the sensory system of electric fish, the auditory system of bats, and the visual system of flies.
		Neural nets that depend on the timing of inter-neuron signals are called pulsed neural nets, and that is the topic of this book.
		Most models of neural nets assume the inter-neuron weights are static; that is, they change only on the slow time scale of learning.
	www.innovatia.com /software/papers/reviews/pulsednets.htm (917 words)

	Using Neural Networks
		A probabilistic neural network (PNN) is another neural network variant that is similar to a BP and has the same general architecture as a BP with a similar information flow.
		Neural networks cannot do anything that cannot be done using traditional computing techniques, but they can do some things that would otherwise be very difficult.
		Neural networks are particularly useful with sensor data; data from a complex chemical, manufacturing, or commercial process; and analog problems.
	pubs.acs.org /hotartcl/tcaw/99/nov/simon.html (2112 words)

	Neural Nets
		Neural nets have the "topology of a directed graph" [Tayl93], meaning that, like the structure of the brain, connections between nodes or neurons is one-way.
		Neural networks are a natural extension of exploring the limits of computing, in terms of methodology and theory.
		Neural nets allow the programming of systems to solve "problems in which the rules governing the situation are unknown or difficult to use" [Tayl93] and to apply computing to problems not solvable in a strictly linear fashion.
	ei.cs.vt.edu /~history/NEURLNET.HTML (1422 words)

	An Overview of Neural Nets (Site not responding. Last check: 2007-10-13)
		Neural nets deal with data in a rows and columns format.
		In neural nets, we call the coefficients weights, and there are a lot more of them.
		Neural network "training" results in mathematical equations (models) just like regression analysis does, but the neural network equations are far more complex than the simple "polynomial" equations that regression produces.
	www.wardsystems.com /info.asp?id=nn101 (527 words)

	Neural Nets
		Neural Nets are simulations of the human brain.
		But a neural net is able to determine the right direction of movements of markets.
		This is the question the investor wants to get an answer about, and this is where we see neural nets being stronger than any other forecast techniques, such as classical technical analysis, candlesticks etc. Therefore, neural nets are perfect systems for the optimum timing of an investment.
	www.webcom.com /~progsys/neural.htm (419 words)

	Notes on Neural Nets
		Neural nets built along the Feed-Forward MLP architecture are already pretty good at ignoring redundant inputs, so one should give them an equal chance to start off with.
		Your neural indicator will be a composite of your inputs, so is likely not to be much more predictive than the inputs you are using, and at the end of the day, often just constitute great multi-dimensional moving averages...
		From experience, using neural nets as classifiers can be far more rewarding, and yet this utilization of neural nets is often overlooked.
	www.foretrade.com /notes_on_neural_nets.htm (618 words)

	GameDev.net - Neural Netware
		Neural nets model the structure of neurons in a crude way, but not the high level functionality of reason and deduction, at least in the classical sense of the words.
		The neural net can learn through experience a set of responses, then when a new experience occurs, the net can respond with something that is the best guess at what should be done.
		And as a neural net is filled with information, the input values that are to be recalled will overlap since the input space can no longer keep everything partitioned in a finite number of dimensions.
	www.gamedev.net /reference/articles/article771.asp (6504 words)

	Neural Networks - Artificial Brains
		To make progress however we must simplify the problem, and so usually we treat the artificial neuron as a more or less simple switch, with interconnections whose strength can be varied at will (a sort of fuzzy logic).
		As yet Neural Network systems are very limited in comparison, but simple network structures are known to have the ability to self-organise.
		In these cases we need the network to recognise features of the input data itself (categorise it) and to display its findings in some way as to be of use (which may include movement or other actions).
	www.calresco.org /neural.htm (1391 words)

	Randomly Wired Neural Nets
		In particular, we wish to remove neurons from the neural net that do not have any potential across them (or, equivalently, any flow through them) when we introduce a potential across two given nodes of the network.
		It is straightforward to show that a neuron in a neural net will support flow if and only if the corresponding edge in the graph model of the neural net lies on some simple path between v and w.
		Thus, the set of all vertices of G that are on at least one simple path between v and w represent the vertices that would be present in a model of the neural net with the unnecessary neurons removed.
	acm.uva.es /p/v5/582.html (570 words)

	The Distributed Chess Project
		Artificial neural nets derive their name from the fact that they are made up by a number of highly interconnected, rather simple nonlinear information processing elements, the artificial neurons, which are supposed to work in a simplified fashion similarly to their natural counterparts in the brain.
		In the context of this project, an artificial neural net is just a fl box that produces a legal chess move from a given chess position.
		That is to say, the neural net takes a chess position as input and somehow comes up with a legal move as output.
	neural-chess.netfirms.com /HTML/project.html (944 words)

	PC AI - Neural Nets
		The neural network is configured for a specific application, such as data classification or pattern recognition, through a learning process called training.
		Neural networks can differ on: the way their neurons are connected; the specific kinds of computations their neurons do; the way they transmit patterns of activity throughout the network; and the way they learn including their learning rate.
		Neural networks are being applied to an increasing large number of real world problems.
	www.pcai.com /web/ai_info/neural_nets.html (1747 words)

	Chaos Theory and Neural Nets
		Neural networks are a relatively new development in computer science, having survived a brush with the exclusive-or problem while the field was still in its teens in the 1960s and recovered for a renaissance in the 1980s.
		Artificial neural systems are attempts to model some of the characteristics of the brain in order to capture and explore those qualities of the brain's reasoning power in which the architecture of the brain is assumed to play a major part.
		Artificial neural systems were designed to capture some of the useful brain functions by modeling the features of the brain.
	nepenthes.lycaeum.org /Misc/chaos.html (3206 words)

	Neural Networks and Dynamical Systems
		Neural networks have been hailed as the paradigm of choice for problems which require "Human Like" perception.
		We are interested in different ways to tease neural networks open, to analyse what they are representing, how they are "thinking".
		With regard to neural networks, the Project investigates the dymanics and capabilities of recurrent neural networks, focusing primarily on temporally-oriented tasks.
	www.demo.cs.brandeis.edu /pr/neural.html (498 words)

	Neural Nets
		NXL is a C language implementation of a neural net which learning algorithm and structure are closely inspired from Scott Fahlman's QuickProp.
		VB BackProp is a small Visual Basic utility for neural net novices who are familiar with Excel and VBA.
		Many neural nets do not generalize well on account of "neuronal glut", and it is always advisable to build smaller nets.
	www.foretrade.com /neural_nets.htm (350 words)

	Neural networks
		The interest in artificial neural nets considerably increased in recent years.
		The last two articles on the fast neural networks with kernel organization are kindly granted for PC Noon by Alexander Dorogov (dorv@lens.spb.ru).
		The destination of the program is a research of neural nets, though it can be used for classification and prediction.
	www.orc.ru /~stasson/neuroe.html (1657 words)

	Cogprints - Subject: Neural Nets (Site not responding. Last check: 2007-10-13)
		Cangelosi, Angelo (2000) Evolution of Symbolisation in Chimpanzees and Neural Nets.
		Marshall, J.A. and Schmitt, C.P. and Kalarickal, G.J. and Alley, R.K. Neural model of transfer-of-binding in visual relative motion perception.
		Cangelosi, A and Parisi, D (1998) The emergence of a "language" in an evolving population of neural networks.
	cogprints.org /view/subjects/comp-sci-neural-nets.html (5382 words)

	Neural Nets
		Neural nets are used by banks for automated teller machines to verify the identity of individuals by evaluating three-dimensional pictures of the individual’s face.
		The theoretical foundations of neural nets are rooted in the works of John von Neumann and Rudolph Ortvay.
		Neural nets have not reached one millionth of the capacity of neural connections that humans possess — although there are neural network connections that are faster than human due to electric synapses, the level of processing power is far inferior to that of humans.
	www.stanford.edu /class/sts129/essays/bereknyei.htm (1891 words)

	Neural Nets
		The central notion of neural net analysis is that we can use a set of observations from the past to predict future relationships.
		At times the solution procedure will converge to a results with a higher norm because neural net estimation problems are sometimes characterized by nonconvexities and may have local optimal solutions which are not the same as the global optimal solution.
		In many neural net data sets the various series may be divergent is magnitude.
	www.eco.utexas.edu /faculty/Kendrick/frontpg/NeuralNets.htm (2196 words)

	Simulation Of Neural Nets
		The neural simulator program is designed to he used as the nervous system for a robot, on which sensors are mounted to encode relationships between the robot and it's environment and internal variables.
		B Both of these parameters-s must be treated in accord with the requirement that each neuron in the net be temporally equivalent and not depend on its's place in the serial execution of the simulation.
		This time must be much smaller than the average interval between neural firing for the simulated organism, and may be regarded as the width of an action potential in real organisms.
	www.virtualschool.edu /cox/pub/70DECUSNeuralNets/index.html (3414 words)

	LVQ Neural Nets
		As a result, the space is partitioned by a ‘Voronoї net’ of hyperplanes perpendicular to the linking line of two CVs (mid-planes of the lines forming the ‘Delaunay net’; see Fig.
		In terms of neural networks a LVQ is a feedforward net with one hidden layer of neurons, fully connected with the input layer.
		A good overview of statistical and neural approaches to pattern classification is given by [48] or [51].
	www.neural-forecasting.com /lvq_neural_nets.htm (949 words)

	Neural Nets (Site not responding. Last check: 2007-10-13)
		This Java applet depicts the use of a neural net to characterize numeric digits as per their actual numeric values.
		Neural nets are being investigated for several applications at APL.
		Perhaps a bit dated, but still full of useful, less-technical answers to the "what is a neural net?" question.
	staff.washington.edu /pmb/NeuralNets.html (228 words)

	NeuroDimension - Neural Network Software, Neural Net Software, Neural Networks, Neural Nets
		Neural networks and genetic algorithms are exciting technologies in the field of artificial intelligence.
		Our neural network software products are among the most powerful and flexible on the market today, yet their intuitive graphical user interfaces make them incredibly easy to use.
		This advanced technical analysis trading software combines neural network and genetic algorithm technologies with traditional technical analysis to create a highly effective tool for financial modeling.
	www.nd.com (512 words)

	Neural Nets C++ library - Rodrigo de Salvo Braz (Site not responding. Last check: 2007-10-13)
		This library defines a set of classes that are essential to neural networks, like pattern, batch and neural net itself.
		It does not define, though, classes for internal components like units, for example, because the implementations of many models do not need that level and it would make the applications be heavier and more complicated than necessary.
		She has to provide the functions that manipulate the "memory" of the model (the weights in the case of neural networks), how it computes its outputs, how it learns, etc.
	bashful.cs.uiuc.edu /~braz/brown_public_html/neural_nets_library.html (280 words)

	Wave59 Technologies
		The trader using the neural net on the chart above is effectively 15 minutes ahead of the trader without it.
		We use a proprietary genetic algorithm to teach the neural net the patterns in the data.
		Chromosomes containing neural network data are mated, mutated, and sorted in the same way as plant and animal life in nature.
	www.wave59.com /neural.asp (1117 words)

	An Introduction to Neural Networks
		Although learning in these nets can be slow, running the trained net is very fast - even on a computer simulation of a neural net.
		Neural networks cannot do anything that cannot be done using traditional computing techniques, BUT they can do some things which would otherwise be very difficult.
		This is one of the first large-scale applications of neural networks in the USA, and is also one of the first to use a neural network chip.
	www.cs.stir.ac.uk /~lss/NNIntro/InvSlides.html (2217 words)

	Neural Nets, Connectionism, Perceptrons, etc.
		The point is that the elements in connectionist models called "neurons" bear only the sketchiest resemblance to the real thing, and neural nets are no more than caricatures of real neuronal circuits.
		Gary William Flake, "The Calculus of Jacobian Adaptation" [Not confined to neural nets]
		The issues of interference among the different circuits that are established, of robustness to noise, and of the stability of the hierarchical memorization process are addressed.
	cscs.umich.edu /~crshalizi/notebooks/neural-nets.html (1511 words)

	Neural Networks ( Neural Network Applications, Artificial Neural Networks, NN, ANN, and Neural Nets) Definition
		A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory.
		A program can then tell the network how to behave in response to an external stimulus (for example, to input from a computer user who is interacting with the network) or can initiate activity on its own (within the limits of its access to the external world).
		Neural networks are sometimes described in terms of knowledge layers, with, in …
	www.bitpipe.com /tlist/Neural-Networks.html (307 words)

Try your search on: Qwika (all wikis)