Five classifiers including the K-means, Fuzzy c-means, K-nearest neighbour, Multi-Layer Perceptron Neural Network and Probabilistic Neural Network classifiers are compared for application to colour grade classification and detection of bruising of Granny Smith apples. A number of suitable discriminate features are determined heuristically for the categorisation of four classes including: high g...

In this study, the automated diagnostic systems employing diverse and composite features for electrocardiogram (ECG) signals were analyzed and their accuracies were determined. In pattern recognition applications, diverse features are extracted from raw data which needs recognizing. Combining multiple classifiers with diverse features are viewed as a general problem in various application areas...

Thomas Kailath Inform. Systems Lab. Stanford University Stanford, Calif. 94305 A rigorous analysis on the finite precision computational <)Spects of neural network as a pattern classifier via a probabilistic approach is presented. Even though there exist negative results on the capability of perceptron, we show the following positive results: Given n pattern vectors each represented by en bits ...

In this paper, learning algorithm for a multiplicative neural network motivated by spiking neuron model (MSN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is observed that the inclusion of a few more biological phenomena in the formulation of artificial neural network models make them more prevailing. Several bench...

In this paper, a new powerful method in artificial neural networks, called modular network SOM (mnSOM) is introduced. mnSOM is a generalization of Self Organizing Maps (SOM) formed by replacing each vector unit of SOM with function module. The modular function could be a multi layer perceptron, a recurrent neural network or even SOM itself. Having this flexibility, mnSOM becomes a new powerful ...

The model of the hybrid neural network is considered. This model consists of model ART-2 for clustering and perceptron for preprocessing of images. The perceptron provides invariant recognition of objects. This model can be used in mobile robots for recognition of new objects or scenes in sight the robot during his movement.

In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from so...

This paper presents a massively parallel method for classifying electroencephalogram (EEG) signals based on min-max modular neural networks. The method has several attractive features. a) A largescale, complex EEG classification problem can be easily broken down into a number of independent subproblems as small as the user needs. b) All of the subproblems can be easily learned by individual sma...