Performance enhancement for fuzzy adaptive resonance theory (ART) neural networks - Electronics Letters
نویسنده
چکیده
Introduction: The adaptive rcsonance theory neural network (ART) as suggested by Grossberg [l] is a useful tool for pattern recognition. The original ART architecture (ART1 j, however, is capable of processing binary inputs only. The fuzzy ART [2] overcomes some of the limitations of ART1. While it has a similar architecture to that of ARTI, it can process continuous valued data, and has a fast and stable learning procedure. The proposed feature-adaptive fuzzy ART self-organises not only the network weights but also the number of features at the input layer.
منابع مشابه
INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...
متن کاملTowards the experimental evaluation of novel supervised fuzzy adaptive resonance theory for pattern classification
This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and competitive neural trees (CNeT) Networks over three pattern recognition problems. We have used two well-known patterns (IRIS and Vowel data) and a biological data (hydrogen data) to evaluate and check SF-ART stability, reliability, learning speed and computati...
متن کاملCategory Art: a Variation on Adaptive Resonance Theory Neural Networks
In this paper we describ� Category ART, a variation on the adaptive resonance theory (ART) neural network models. Category ART is a predictive ART architecture because it incorporates an ART module to be able to learn to predict a prescribed category given a prescribed n-dimensional input vector a. In contrast to ARTMAP, Category ART contains only one ART module and the map field algorithm has ...
متن کاملComparative Analysis of Fuzzy ART and ART-2A Network Clustering Performance - Neural Networks, IEEE Transactions on
Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. Many different types of ART-networks have been developed to improve clustering capabilities. In this paper we compare clustering performance of different types of ART-networks: Fuzzy ART, ART 2A with and without complem...
متن کاملFuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a...
متن کامل