
	Where results make sense

Topic: Artificial neural network

Related Topics

Neural networks

Instantaneously trained neural networks

Boltzmann machine

Connectionism

Unsupervised learning

Logit

Local minima

Machine learning

Artificial intelligence

Computer scientist

Quantum computer

Data mining

Simulated annealing

List of computer term etymologies

Time series

In the News (Wed 25 Nov 09)

Northern Trust

Marvin Harrison

Peter Chernin

Gary Locke

Match Play

Penelope Cruz

Mickey Rourke

AR Rahman

Danny Boyle

Hugh Jackman

	Artificial neural network - Psychology Wiki - a Wikia wiki
		A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.
		These networks are also similar to the biological neural networks in the sense that functions are performed collectively and in parallel by the units, rather than there being a clear delineation of subtasks to which various units are assigned (see also connectionism).
		Neural network software is used to simulate, research, develop and apply artificial neural networks, biological neural networks and in some cases a wider array of adaptive systems.
	psychology.wikia.com /wiki/Artificial_neural_network (5231 words)

	Neural Networks
		Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.
		The behaviour of an ANN (Artificial Neural Network) depends on both the weights and the input-output function (transfer function) that is specified for the units.
		Neural networks were used to discover the influence of undefined interactions by the various variables.
	www.doc.ic.ac.uk /~nd/surprise_96/journal/vol4/cs11/report.html (6614 words)

	Artificial Neural Network Modelling (Site not responding. Last check: )
		Neural networks have been used as direct models of sensory processing, whereby the network is used to model the computational processes of the brain.
		Neural networks can be used in a general way to explore how sensory and perceptual processes might influence the evolution of biological signals.
		Neural networks have provided a useful alternative, and perform well when the input is noisy and there is no a priori basis for classification.
	galliform.bhs.mq.edu.au /~richard/Research/model/model_animbehav.html (1004 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 (501 words)

	Artificial Neural Network based classification using Textural Features for Remotely Sensed Data
		Neural networks are valuable tools in problems when one has little or no knowledge about the form of the relationship between input vectors and their corresponding outputs.
		Artificial neural networks[6][8] can be seen as highly dynamical systems consisting of multiple simple units that can perform transformation by means of their state response to their input information.
		Features extracted from the image were applied to the Neural Network that was designed to have 8 neurons in the input layer,10 neurons in the hidden layer and 5 neurons the output layer.The original image is shown in figure 4a and the output image of the proposed method is shown in figure 4b.
	www.mapindia.org /2005/papers/AFG/192.htm (2161 words)

	Training an Artificial Neural Network (Site not responding. Last check: )
		Artificial neural networks offer a completely different approach to problem solving and they are sometimes called the sixth generation of computing.
		Neural networks are structured to provide the capability to solve problems without the benefits of an expert and without the need of programming.
		A comparison of artificial intelligence's expert systems and neural networks is contained in Table 2.6.2.
	www.dacs.dtic.mil /techs/neural/neural3.html (1944 words)

	Artificial Neural Networks Technology
		Neural networks are now globally recognized as the most effective and appropriate artificial intelligence technology for pattern recognition.
		In the past, most failures in using neural networks were attributable to the users’ poor skills in the appropriate preparation of data and neural network design.
		Neural networks are data analysis methods and algorithms, indirectly based on the nervous systems of humans and animals.
	alyuda.com /neuralnetworks.htm (484 words)

	artificial neural network - HighBeam Encyclopedia
		Use of artificial neural networks and genetic algorithms--experiences from a tablet formulation.
		Artificial neural networks for the prediction of shear capacity of steel plate strengthened RC beams.(finite element method)
		Artificial neural network approach for grading of maintainability in wet areas of high-rise buildings.
	www.encyclopedia.com /doc/1O87-artificialneuralnetwork.html (498 words)

	What is an artificial neural network?
		An artificial neural network (ANN) is a computer simulation of a "brainlike" system of interconnected processing units.
		The behaviour of a single processing unit in an ANN can be characterized as follows: First, the unit computes the total signal being sent to it by other processors in the network.
		The pattern of connectivity in an ANN (i.e., the strengths of the connections between various processing units) defines the causal relations between the network's processors, and is therefore analogous to a program in a conventional computer (e.g., Smolensky, 1988).
	www.bcp.psych.ualberta.ca /about/ann.html (230 words)

	NeuroSolutions: What is a Neural Network?
		The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled.
		This error is then fed back (backpropagated) to the neural network and used to adjust the weights such that the error decreases with each iteration and the neural model gets closer and closer to producing the desired output.
		The binary data is then fed into a neural network that has been trained to make the association between the character image data and a numeric value that corresponds to the character.
	www.nd.com /welcome/whatisnn.htm (972 words)

	generation5 - An Introduction to Neural Networks
		This area of neural networking is the "fuzziest" in terms of a definite set of rules to abide by.
		Neural networks are designed to work with patterns - they can be classified as pattern classifiers or pattern associators.
		Other problems with neural networks are the lack of defining rules to help construct a network given a problem - there are many factors to take into consideration: the learning algorithm, architecture, number of neurons per layer, number of layers, data representation and much more.
	www.generation5.org /content/2000/nnintro.asp (1243 words)

	Introduction to Neural Networks and Knowledge Modeling
		Neural networks are a branch of the field known as "Artificial Intelligence".
		In a nutshell a Neural network can be considered as a fl box that is able to predict an output pattern when it recognizes a given input pattern.
		Once trained, the neural network is able to recognize similarities when presented with a new input pattern, resulting in a predicted output pattern.
	www.makhfi.com /tutorial/introduction.htm (921 words)

	Artificial neural network simulator - cookbook, exercise 1
		The exercise in datadriven neural networks concerns prediction of protein secondary structure from the linear sequence of amino acids.
		The exercise is carried out using a simple neural network simulator - howlite - and ready made sequence data constructed by conversion of Brookhaven coordinate data into secondary structure assignments by the Kabsch and Sander DSSP program.
		, a neural network that predicts whether a given amino acid belongs to a helix or not.
	www.cbs.dtu.dk /biotools/how (880 words)

	Artificial Neural Network: An Introduction
		Neural nets, originally conceived as an attempt to model the biophysical aspects of human brain, have received ample recognition in various fields of engineering.
		One of the strong non-linear regression techniques, neural networks, is an information processing system with an adaptability and ability to learn.
		Neural networks adaptively estimate continuous functions from data without specifying mathematically how outputs depend on inputs.
	www.andrew.cmu.edu /user/smisra/ANN.htm (922 words)

	Mind Hacks: Fast Artificial Neural Network Library
		The Fast Artificial Neural Network Library is a programming library that takes much of the pain out of constructing artificial intelligence and cognitive modelling projects.
		Neural networks are used both as software tools for completing otherwise difficult tasks, and in cognitive science for simulating cognitive processes.
		In neuropsychology, neural networks are often created to simulate a certain cognitive task, and then the network is 'damaged' to see whether the network can predict the effects of brain injury or impairment.
	www.mindhacks.com /blog/2006/04/fast_artificial_neur.html (219 words)

	Artificial Neural Network Training (Site not responding. Last check: )
		This is a diagram of the neural net which I trained most sucessfully to the data.
		This neural net is able to train within bounds (all nodes have RMS less than or equal to 0.1) within about 41,000 backpropogations.
		This net is similar to the final net I chose to use, however instead of having 16 nodes with 2 inputs each as the hidden layer, there are only 2 nodes with 17 inputs each.
	cs-people.bu.edu /tpease/pp3/arch.html (444 words)

	Artificial Neural Networks
		In order to have a good understanding of how an artificial neural network could create a machine that is capable of learning, it is important to first understand how organic neural networks function. The importance of understanding organic intelligence stems from the fact that non-organic brains use organic minds as a blueprint.
		Artificial neural networks get their power from the fact that billions of such artificial neurons are combined into miniature networks. Such complex networks of simple components could give future computers the ability to think and reason on their own.
		· Supervised- This teaching method gives the neural network both the inputs and outputs for a desired problem. It is then the network’s responsibility to process the inputs and compare the results to the given outputs. Any errors that occur in the processing of the inputs are refined until the correct outputs are achieved.
	komar.cs.stthomas.edu /qm425/02s/Lloyd2.htm (482 words)

	Neural Networks - Introduction (Site not responding. Last check: )
		However, bear in mind that neural network-type algorithms are based on a specific nature-inspired architecture, whereas other learning algorithms such as genetic algorithms are based on other architectures that cannot strictly be called neural networks.
		Neural Networks are one of the more popular and established types of artificial learning, but the last 20 years has seen an explosion of other types of learning algorithms, many inspired by nature.
		Generally speaking, a network is trained with a dataset and then the mature network is queried with other datasets.
	www.ucl.ac.uk /oncology/MicroCore/HTML_resource/N_Net_Intro.htm (330 words)

	IEI's Patented Self-Training Artificial Neural Network Objects (STANNO)
		In effect, it was a neural network (neurons, and connections, sans the traditional training algorithm) that could spontaneously absorb knowledge.
		Details - When a neural network expert talks about training a net, what he or she means is that a very cleverly contrived computer algorithm is being used to 'mathematically punish and reward' the connection weights within that neural net, forcing it to accurately absorb memories and complex relationships inherent within its training patterns.
		Rather than being simple neural cascades composed of interwoven neural network modules that are pre-trained and static, these SuperNets, as they are called, are composed of individual neural network modules that are training in situ, in real time.
	www.imagination-engines.com /stanno.htm (732 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 (312 words)

	Fast Artificial Neural Network Library
		Please note, that it is not possible to train a network when using the threshold activation function, due to the fact, that it is not differentiable.
		A fully connected network with shortcut connections, is a network where all neurons are connected to all neurons in later layers.
		A fully connected ann with shortcut connections is an ann where neurons have connections to all neurons in all later layers.
	fann.sourceforge.net /fann.html (7721 words)

	artificial neural network (Site not responding. Last check: )
		A neural network is a processing device, either an algorithm, or actual hardware, whose design was inspired by the design and functioning of animal brains and components thereof.
		Most neural networks have some sort of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns.
		The term "neural net" should logically, but in common usage never does, also include biological neural networks, whose elementary structures are far more complicated than the mathematical models used for ANNs.
	www.cacs.louisiana.edu /~mgr/404/burks/foldoc/42/7.htm (241 words)

	Fast Artificial Neural Network Library
		-- Create a new artificial neural network, and return a pointer to it.
		-- Create a new artificial neural network with shortcut connections, and return a pointer to it.
		-- Reset the mean square error of an ANN.
	leenissen.dk /fann/fann_1_2_0 (542 words)

	Neural Network Trading Systems
		We accomplish this through the use of various neural networks and proprietary algorithms artificial intelligence if you will.
		I believe that artificial intelligence may be able to solve many business problems and you have found a very profitable application for neural networks...
		Robert Hesler, our neural network designer, is a private investor with over 35 years experience trading the stock market.
	www.neuralnettradingsystems.com (1219 words)

	Artificial Neural Network - Function Approximation
		The functions are approximated by this applet INSIDE the domain of OPEN interval (0, 1), due to the use of hard-limiting functions in the neural network.
		A speed up in CPU time can either be obtained by using a true neural network computer, or using distributed computing technique where multiple computers are programmed into cooperation to achieve parallelism.
		Error produced in this neural network approximator is inversely proportional to the number of neurons used in the hidden layer.
	www.loyno.edu /~li/home/java/ann (690 words)

	Artificial Neural Network
		An artificial neural network is defined as a structure composed of a number of interconnected units or artificial Neurons.
		For example, the desired function may be specified by enumerating a set of stable network states, or by identifying a desired network output as a function of the network inputs and current states.
		Self-Organizing Networks: These networks exemplify neural implementations of unsupervised learning in the sense that they typically self-organize input patterns into classes or clusters based on some form of similarity.
	www.ewh.ieee.org /r10/bombay/news3/page2.html (472 words)

Try your search on: Qwika (all wikis)