neural network research papers

neural network research papers




artificial-neural-network-2013

artificial-intelligence-neural-network-2014

neural-network-research-papers-31

neural-network-research-papers-23

neural-network-research-papers-22

neural-network-research-papers-21

neural-network-research-papers-14

neural-network-research-papers-13

neural-network-research-papers-12

neural-network-research-papers-11

neural-network-research-papers

neural-network-implementation

spike-based-neural-networks

face-recognition-based-neural-network

face-recognition-using-artificial neural-network

neural-network-and-computer-networks

block-based-neural-network

reconfigurable-artificial-neural-network

hand-gesture-recognition-using-neural-networks

integrating web-mining-and-neural-network-for-personalized-e-commerce-automatic-service

free-research-paper-artificial-intelligence-neural-network

neural-network-approach-to-quantum-chemistry-data-accurate-prediction-of-density-functional-theory-energies

artificial-neural-network-to-predict-skeletal-metastasis-in-patients-with-prostate-cancer

artificial neural-network-modelling-for-the-study-of-ph-on-the-fungal-treatment-of-red-mud

functional-mri-evidence-for-ltp-induced neural-network-reorganization

optimization-and-evaluation-of-a neural-network-classifier-for-pet-scans-of-memory-disorder-subjects

face-recognition-using-principle-component-analysis-eigenface-and neural-network

a neural-network-model-of-adaptively-timed-reinforcement-learning-and-hippocampal-dynamics

assessing-the-effort-of-meteorological-variables-for-evaporation-estimation-by-self-organizing-mapneural-network

neural-network-based-reconstruction-of-a-3d-object-from-a-2d-wireframe

probabilistic-neural-networks

intrusion-detection-using-neural-networks-and-support-vector-machines

artificial-neural-network-approaches-to-intrusion-detection-review

review-and-comparison-of-methods-to-study-the-contribution-of-variables-in-artificial-neural-network-models

recognition-of-plants-by-leaves-digital-image-and-neural-network

fingerprint-identification-and-verification-system-by-minutiae-extraction-using-artificial-neural-network

fingerprint-recognition-using-neural-network

passport-recognition-using-neural-networks

neural-networks-and image-processing

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
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A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by learning without a teacher , and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes

A hierarchical neural-network model for control and learning of voluntary movement
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In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body

The Neural Network House: An Environment hat Adapts to its Inhabitants
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ABSTRACT Although the prospect of computerized homes has a long history, ho/ne automation has never become terribly popular because the benefits are seldom seen to outweigh the costs. One significant cost of an automated home is that someone has to program it to

Snns (stuttgart neural network simulator)
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We here describe SNNS, a neural network simulator for Unix workstations that has been developed at the University of Stuttgart, Germany. Our network simulation environment is a tool to generate, train, test, and visualize artificial neural networks. The simulator consists

A neural network approach to topic spotting
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ABSTRACT This paper presents an application of nonlinear neural networks to topic spotting. Neural networks allow us to model higherorder interaction between document terms and to simultaneously predict multiple topics using shared hidden features. In the context of this

Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot
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ABSTRACT The paper describes the results of the evolutionary development of a real, neural- network driven mobile robot. The evolutionary approach to the development of neural controllers for autonomous agents has been success fully used by many researchers, but

A Bayesian neural network method for adverse drug reaction signal generation
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ABSTRACT Objective: The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million

Network model of shape-from-shading: neural function arises from both receptive and projective fields
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It is not known how the visual system is organized to extract information about shape from the continuous gradations of light and dark found on shaded surfaces of three-dimensional objects1 2. To investigate this question3-4, we used a learning algorithm to construct a

Self-organizing neural network that discovers surfaces in random-dot stereograms
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The standard form of back-propagation learning1 is implausible as a model of perceptual learning because it requires an external teacher to specify the desired output of the network. We show how the external teacher can be replaced by internally derived teaching signals.

Feedback-error-learning neural network for supervised motor learning
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ABSTRACT In supervised motor learning, where the desired movement pattern is given in taskoriented coordinates, one of the most essential and difficult problems is how to convert the error signal calculated in the task space into that of the motor command space. We

Intermanual coordination: from behavioural principles to neural-network interactions
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Locomotion in vertebrates and invertebrates has a long history in research as the most prominent example of interlimb coordination. However, the evolution towards upright stance and gait has paved the way for a bewildering variety of functions in which the upper limbs

Training a 3-node neural network is NP-complete
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We show for many simple two-layer networks whose nodes compute linear threshold functions of their inputs that training is NP-complete. For any training algorithm for one of these networks there will be some sets of training data on which it performs poorly, either

Empirical studies on the speed of convergence of neural network training using genetic algorithms
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ABSTRACT This paper reports several experimental results on the speed of convergence of neural network training using genetic algorithms and back propagation. Recent excitement regarding genetic search lead some researchers to apply it to training neural networks.

Neural Network Toolbox
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This chapter has a number of objectives. First, it introduces you to learning rules, methods of deriving the next changes that might be made in a network, and training, a procedure whereby a network is actually adjusted to do a particular job. Along the way, this chapter

Pairwise neural network classifiers with probabilistic outputs
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ABSTRACT Multi-class classification problems can be efficiently solved by partitioning the original problem into sub-problems involving only two classes: for each pair of classes, a (potentially small) neural network is trained using only the data of these two classes. We

HIDE: a hierarchical network intrusion detection system using statistical preprocessing andneural network classification
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ABSTRACT In this paper we introduce the Hierarchical Intrusion DEtection (HIDE) system, which detects network-based attacks as anomalies using statistical preprocessing and neural network classification. We describe our system architecture and the statistical

A neural network model with dopamine-like reinforcement signal that learns a spatial delayed response task
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ABSTRACT This study investigated how the simulated response of dopamine neurons to reward-related stimuli could be used as reinforcement signal for learning a spatial delayed response task. Spatial delayed response tasks assess the functions of frontal cortex and

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