neural network research papers-21




Neural network, genetic, and fuzzy logic models of spatial interaction
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ABSTRACT. The author investigates the extent to which smart computational methods can be used to create new and better performing types of spatial interaction model. He briefly describes the application of three different computationally intensive modelling

Macroscopic modeling of freeway traffic using an artificial neural network
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Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macroscopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabilities in their

Data preparation for a neural network.
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Are you preparing to implement neural-net application ideas? It s important to assess the available data properly before making any project commitments. The data may need to be converted into another form to be meaningful to a neural network.(I ll treat the neural

Neural network agents for learning semantic text classification
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The research project AgNeT develops Agents for Neural Text routing in the internet. Unrestricted potentially faulty text messages arrive at a certain delivery point (eg email address or world wide web address). These text messages are scanned and then

Design and development of an artificial neural network for estimation of formation permeability
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ABSTRACT Permeability is one of the most important characteristics of hydrocarbon bearing formations. An accurate knowledge of permeability provides petroleum engineers with a tool for efficiently managing the production process of a field. Furthermore, It is one of the most

FANNC: A fast adaptive neural network classifier
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ABSTRACT. In this paper, a fast adaptive neural network classifier named FANNC is proposed. FANNC exploits the advantages of both adaptive resonance theory and field theory. It needs only one-pass learning, and achieves not only high predictive accuracy but also fast

Neural network prediction in a system for optimizing simulations
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Neural networks have been widely used for both prediction and classification. Back- propagation is commonly used for training neural networks, although the limitations associated with this technique are well documented. Global search techniques such as

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization
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Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons,

Comparison of genetic algorithm and particle swarm optimizer when evolving a recurrent neural network
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This paper compares the performance of GAs and PSOs in evolving weights of a recurrent neural network. The algorithms are tested on multiple network topologies. Both algorithms produce successful networks. The GA is more successful evolving larger networks and the

Visualizing high-dimensional structure with the incremental grid growing neural network
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Real world data is often very high-dimensional, and often has a structure that is difficult both to recognize and describe. When presented as a set of high-dimensional vectors in tabular form, the relationships between data items may be difficult to fathom. For instance, human

Neural network constructive algorithms: trading generalization for learning efficiency?
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There are currently several types of constructive,(or growth), algorithms available for training a feed-forward neural network. This paper describes and explains the main ones, using a fundamental approach to the multi-layer perceptron problem-solving mechanisms. The

Neural network and genetic programming for modelling coastal algal blooms
In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics. In the present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of algal

A study of experimental evaluations of neural network learning algorithms: Current research practice
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A large body of research in arti cial neural networks is concerned with nding good learning algorithms to solve practical application problems. Such work tries to improve for instance the quality of found solutions generalization, the probability of convergence, the ease of

Influence of missing values on artificial neural network performance
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ABSTRACT The problem of databases containing missing values is a common one in the medical environment. Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments. Artificial neural networks (ANNs)

Economic forecasting: Challenges and neural network solutions
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ABSTRACT Macroeconomic forecasting is a very difficult task due to the lack of an accurate, convincing model of the economy. The most accurate models for economic forecasting, black box time series models, assume little about the structure of the

Electronic nose and neural network use for the classification of honey
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Abstract Seventy samples of honey of different geographical and botanical origin were analysed with an electronic nose. The instrument, equipped with 10 Metal Oxide Semiconductor Field Effect Transistors (MOSFET) and 12 Metal Oxide Semiconductor (

character extraction using neural network



Optical Character Recognition Using Novel Feature ExtractionNeural NetworkClassication Techniques
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Abstract This paper describes two novel techniques applied to the feature extraction and pattern classication stages in an OCR system for tgpeset characters. A technique for estimating the class discrimination ability of continuous valued features is presented

Handwritten English character recognition using neural network
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provides good recognition accuracy of more than 70% of Handwritten English characters. Keywords Handwritten Character Recognition, Feature Extraction, Back propagation network, Multilayer Perceptronis defined as simultaneously projecting a point of the character into its

Use of artificial neural network in pattern recognition
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During feature extraction, the normalized image is represented as feature vectors.are: an optimal selection of features which categorically defines the details of the characters, the numberThe character from the scanned image has been normalized from 60 X 60 pixel into 32 X

Artificial neural networks: A tutorial
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Well-known applications include ' character recognition, speech recognition, EEG waveform classification, blood cellModeling a biological nervous system using ANNs can also increase our understanding ofAn obvious one is to use activation functions other than the threshold

Convolutional networks for images, speech, and time series
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recognizing spatial or temporal objects. Convolutional networks force the extraction of localused in character recognition, and in image preprocessing applications (Boser et al., 1991). Speeds of more than 1000 characters per second were obtained with a networkwith around

Neural network fingerprint classification
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Neuralnetworks have been successful in engineering applications such as character recognition, speech recognitiontwo classes are fairly well separated; only a small number of characters fall outside With the evolved KL feature extraction, the network may be regarded as the

Optical character recognition (OCR) for printed devnagari script using artificial neural network
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Feature Extraction Feature extraction is one of the most important steps in developing adescribes the various features selected by us for classification of the selected characters.concept can be applied successfully to solve the Devnagari Optical Character Recognition Problem. There has been a recent surge in publications using the PCNN or ICM and a few of these have the theory and then explore its known image pro- cessing applications: segmentation, edge extraction, texture extraction, object identification21 2.14 Accessing characters in a string .

Handwritten character recognition using neural network
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Now we divide this 70 X 80 into sub Matrix of 7 X 8. We extract each sub matrix and calculate the no. of ones in that sub matrix.We are performing the test on only Capital characters so the outputs of the NeuralNetworks are automated character extraction

Extracting rules from artificial neural networks with distributed representations
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of speech generation [12] and recognition [18], vision and robotics [8], handwritten character recognition [5rule extraction techniques to deep networks, in which approximate rule extraction methods can Using sampling and queries to extract rules from trained neuralnetworks.

Automatic scene text recognition using a convolutional neural network
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This is a matching measure between the binary character image and a template image.learnt operations ensure the extrac- tion of robust features, leading to the automatic recognition of characters in naturalLEVEL 1: Feature extraction level, relying on the C1 and S1 layers.

Semantic integration in heterogeneous databases using neural networks
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But for the field Stud-Id whose data type is designed as character (eg 999-99-9999total charac- ters: A Last-Name or First-Name field will con- tain few white-space characters.8We are currently developing parsem which extract infor- mation from Ingres, Oracle, and IBM AS/400

Approach to recognition of license plate numbers using neural networks
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image is then more suitable for further image- processing tasks such as segmentation, feature extraction and object5. Extracting the character from license plate networks for recognizing of license plate characters, taking into account their properties to be as an associative 2.2 Feature Extraction _ combination of features computed from each column in the given preprocessed image.The result is a set of initial segments that generally con- sist of images of one or more characters. Those consisting of more than one character need to be split.

Character Recognition Using Matlab s Neural Network Toolbox
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The systems have the ability to yield excellent results. The feature extraction step of optical character recognition is the most important.A simplistic approach for recognition of Optical charactersusing artificial neuralnetworks has been described. Page 7.

Feature extraction for character recognition using Gabor-type filters implemented by cellular neural networks
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ABSTRACT: This paper proposes an approach for feature extraction using a CNN Gabor filter and an orientation map. We use a set of hand-written characters for testing the complete system. The frequency response of the CNN Gabor-type filter and the filter

A multistage handwritten Marathi compound character recognition scheme using neural networks and wavelet features
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The characters are then normalized to a fixed size of 16x16 after structural classification for feature extraction. A single level wavelet decomposition of the resized character image generates the approximation and the detail coefficients.

Optical Character Recognition of Bangla Characters using neural network: A better approach
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Algorithm Pattern Extraction For each line detected While search_is _within_the _line _boundary Whilethinning better approach in scaling and neuralnetwork in detection of characters .[2] MR Hasan MA Haque and SUF Malik, Bangla Optical Character Recognition System

A simple segmentation approach for unconstrained cursive handwritten words in conjunctionwith the neural network
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On the other hand researchers have employed artificial neuralnetworks, hidden Morkov models, statistical classifiers etc to extract rules based on numerical data [16-21, 36-37] ThisSegmentation of merged characters by neural "A contour characterextraction approach in

A contour code feature based segmentation for handwriting recognition
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Figure 7 Contour code feature The input to the contour code feature extraction module is the set of coordinate (x, y) of the contour extracted from the contour extraction phase.each character of each lexicon word to the characters in the test word being examined.

Globally trained handwritten word recognizer using spatial representation, convolutionalneural networks, and hidden Markov models
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An elastic word model eg, an HMM can extract word candidates from the network output.Input normalization reduces intra-character variability, simplifying character recognition.The recognition of handwritten characters from a pen trajectory on a digitizing surface is often

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