# neural network research papers-12

**Self-organization for object extraction using a multilayer neural network and fuzziness measures**

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ABSTRACT The feedforward multilayer perceptron (MLP) with back-propagation of error is described. Since use of this network requires a set of labeled input-output, as such it cannot be used for segmentation of images when only one image is available.(However, if

**Global attractivity in delayed Hopfield neural network models**

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ABSTRACT. Two different approaches are employed to investigate the global attractivity of delayed Hopfield neural network models. Without assuming the monotonicity and differentiability of the activation functions, Liapunov functionals and functions (combined

**Extracting symbolic rules from trained neural network ensembles**

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Neural network ensemble can significantly improve the generalization ability of neural network based systems. However, its comprehensibility is even worse than that of a single neural network because it comprises a collection of individual neural networks. In this

**Editing training data for kNN classifiers with neural network ensemble**

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Since kNN classifiers are sensitive to outliers and noise contained in the training data set, many approaches have been proposed to edit the training data so that the performance of the classifiers can be improved. In this paper, through detaching the two schemes adopted

**Predicting Indian monsoon rainfall: a neural network approach**

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The summer monsoon rainfall over India is predicted by using neural networks. These computational structures are used as a nonlinear method to correlate preseason predictors to rainfall data, and as an algorithm for reconstruction of the rainfall time-series intrinsic

**A neural network approach for modeling nonlinear transfer functions: Application for wind retrieval from spaceborne scatterometer data**

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ABSTRACT The present paper shows that a wide class of complex transfer functions encountered in geophysics can be efficiently modelled by the use of neural networks. Neural networks can approximate numerical and non numerical transfer functions. They provide

**Reducing fitness evaluations using clustering techniques and neural network ensembles**

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In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models can be constructed for fitness evaluations to assist the evolutionary algorithms. When

**A neural network model for cursive script production**

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This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a hand with redundant degrees of

**A neural network model of speech acquisition and motor equivalent speech production**

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This article describes a neural network model that addresses the acquisition of speaking skills by infants and subsequent motor equivalent production of speech sounds. The model learns two mappings during a babbling phase. A phonetic-to-orosensory mapping

**Associative memory on a small-world neural network**

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We study a model of associative memory based on a neural network with small-world structure. The efficacy of the network to retrieve one of the stored patterns exhibits a phase transition at a finite value of the disorder. The more ordered networks are unable to

**The effect of neural-network structure on a multispectral land-use/land-cover classification**

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ABSTRACT While neural networks are now an accepted alternative to statistical multispectral classification techniques for remote sensing image classification, the network approach presents both unique challenges and abilities. The size of the hidden layer must be

**Refining neural network predictions for helical transmembrane proteins by dynamic programming**

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ABSTRACT For transmembrane proteins experimental determination of three-dimensional structure is problematic. However, membrane proteins have important impact for molecular biology in general, and for drug design in particular. Thus, prediction method are needed.

**Comparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change**

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ABSTRACT Urbanization has profound effects on the environment at local, regional, and global scales. Effective detection of urban change using remote sensing data will be an essential component of global environmental change research, regional planning, and natural

**Temperature profiling with neural network inversion of microwave radiometer data**

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ABSTRACT A neural network is used to obtain vertical profiles of temperature from microwave radiometer data. The overall rms error in the retrieved profiles of a test dataset was only about 8% worse than the overall error using an optimized statistical retrieval. In

**Facial expression recognition using a neural network**

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ABSTRACT We discuss the development of a neural network for facial expression recognition. It aims at recognizing and interpreting facial expressions in terms of signaled emotions and level of expressiveness. We use the backpropagation algorithm to train the system to

**Genetic weight optimization of a feedforward neural network controller**

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ABSTRACT The optimization of the weights of a feedforward neural network with a genetic algorithm is discussed. The search by the recombination operator is hampered by the existence of two functional equivalent symmetries in feedforward neural networks. To

**An artificial maximum neural network: a winner-take-all neuron model forcing the state of the system in a solution domain**

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A maximum neuron model is proposed in order to force the state of the system to converge to the solution in neural dynamics. The state of the system is always forced in a solution domain. The artificial maximum neural network is used for the module orientation problem

**Neural network-based analysis of MR time series**

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Clustering has been introduced to analyze fMRI data by means of partitioning data into time series of similar temporal behavior. It is hoped that one of these clusters represents a dynamic effect of interest, like functional activation. Using self-organizing maps for

**Minimal topology for a radial basis functions neural network for pattern classification**

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In this paper, after a brief overview of the principal trends in radial basis functions neural networks, we propose a solution for finding the minimal number of hidden units for a radial basis functions structure and we apply this algorithm to different artificial and real tasks.

**A neural network based system for intrusion detection and classification of attacks**

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ABSTRACT With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems.

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neural network research papers-11

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