in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...

The present paper attempts to introduce a new approach for the automatic detection of epileptic spikes in EEG signal which plays a vital role in diagnosing epilepsy. In this approach, we have detected and classified epileptic spikes by using extracted features and Fuzzy ARTMAP neural network. The performance of classifying system is evaluated by three criteria of sensitivity, selectivity and sp...

In this paper, we report on experiments in which we used neural networks for statistical anomaly intrusion detection systems. The five types of neural networks that we studied were: Perceptron; Backpropagation; PerceptronBackpropagation-Hybrid; Fuzzy ARTMAP; and Radial-Based Function. We collected four separate data sets from different simulation scenarios, and these data sets were used to test...

Reducing the computational complexity is desired in speech coding algorithms. In this paper, three neural gain predictors are proposed which can function as backward gain adaptation module of low delay-code excited linear prediction (LD-CELP) G.728 encoder, recommended by International Telecommunication Union-Telecom sector (ITU-T, formerly CCITT). Elman, multilayer perceptron (MLP) and fuzzy A...

We will focus here on approximating functions that map from the vector-valued real domain to the vector-valued real range. A Fuzzy ARTMAP (FAM) architecture, called Fuzzy Artmap with Relevance factor (FAMR, defined in [1]) is considered here as an alternative to function approximation. FAMR uses a relevance factor assigned to each sample pair, proportional to the importance of the respective pa...

Feature selection is more accurate technique in protein sequence classification. Researchers apply some well-known classification techniques like neural networks, Genetic algorithm, Fuzzy ARTMAP, Rough Set Classifier etc for extracting features.This paper presents a review is with three different classification models such as fuzzy ARTMAP model, neural network model and Rough set classifier mod...

A new architecture, called MicroARTMAP, is proposed to impact the category proliferation problem present i n F uzzy ARTMAP. It handles probabilistic information through the optimization of the mutual information between the input and output spaces, but allowing a small training error, thus avoiding overrtting. While reducing the number of categories used by F uzzy ARTMAP, it holds several desir...

This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neural-network learning and recognition. Cascade ARTMAP, a generalization of fuzzy ARTMAP, represents intermediate attributes and rule cascades of rule-based knowledge explicitly and performs multistep inferencing. A rule insertion algorithm translates if-the...

In this paper we present a modification of the test phase of ARTMAP-based neural networks that improves the classification performance of the networks when the patterns that are used for classification are extracted from noisy signals. The signals that are considered in this work are textured images, which are a case of 2D signals. Two neural networks from the ARTMAP family are examined, namely...

the major aim of processing satellite images is to prepare topical and effectivemaps. the selection of appropriate classification methods plays an important role. amongvarious methods existing for image classification, artificial neural network method is ofhigh accuracy. in present study, tm images of 1987, and etm+ images of 2000 and 2006were analyzed using artificial fuzzy artmap neural netwo...