Multilayer Perceptron Neural Network Classifier Research Articles

A new digital modulation recognition method using features extracted from GAR model parameters

Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognition method is proposed in this paper. Because of the better noise suppression ability of the GAR model and the powerful pattern classification capacity of the MLP neural network classifier, the new method can significantly improve the recognition performance in lower SNR with better robustness. To assess the performance of the new method, computer simulations are also performed.

Learning object models from graph templates

This paper describes a novel neural network (NN) based system for detecting rigid objects from their (2-D) gray-level image images. In this approach a labeled graph is employed to construct a template model for an object from the image region where the object is located. A novel network of NN (NoNN) is proposed to learn the examples of object model graph templates. The NoNN is composed of a set of subnetworks that are not connected to one another. The selected network topology improves the generalization of the classifier in terms of its Vapnik-Chervonenkis dimension (VCdim). Each subnetwork is a network of multilayer perceptron neural network classifiers operating in parallel with the rest of the system. Each subnetwork is assigned to learn the label of one vertex of a graph. The detection scheme combines the decisions of the subnetworks to classify an image graph extracted from an input image block. This visual computational model is potentially useful for partial matching where the object is occluded. Performance of the system is tested in modeling and detection of human eye regions in face images with some degree of variation in the direction of pose.

Clinical Diagnosis of Creutzfeldt-Jakob Disease Using a Multi-Layer Perceptron Neural Network Classifier

Article Clinical Diagnosis of Creutzfeldt-Jakob Disease Using a Multi-Layer Perceptron Neural Network Classifier was published on June 1, 1997 in the journal Journal of Intelligent Systems (volume 7, issue 1-2).

HUMAN CHROMOSOME CLASSIFICATION USING MULTILAYER PERCEPTRON NEURAL NETWORK

A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10-20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.

Optical character recognition by a neural network

An optical character recognition (OCR) system, which uses a multilayer perceptron (MLP) neural network classifier, is described. The neural network classifier has the advantage of being fast (highly parallel), easily trainable, and capable of creating arbitrary partitions of the input feature space. Issues in design of the neural network that we examine include the selection of input features, the choice of network learning and momentum parameters, and the selection of training patterns. We also provide a detailed analysis of the learning parameters to provide insight into the MLP, and to suggest a mechanism to automatically tune these parameters. An OCR neural network classifier was trained to recognize characters from a large number of fonts, thereby approaching an omnifont environment. Samples were selected from over 200 fonts and 50 typical office documents, for a total of 110,000 training patterns. In order to evaluate the performance of the MLP classifier, a comparison is made with a high performance dynamic contour warping (DCW) classifier. The base recognition rate on the test set is 96.7% for the neural network classifier, compared to 95.9% for the DCW classifier.