In this paper we are examining the issue of overtraining in Fuzzy ARTMAP. Over-training in Fuzzy ARTMAP manifests itself in two different ways: (a) it degrades the generalization performance of Fuzzy ARTMAP as training progresses; and (b) it creates unnecessarily large Fuzzy ARTMAP neural network architectures. In this work, we are demonstrating that overtraining happens in Fuzzy ARTMAP and we ...

In this paper we are examining the issue of overtraining in Fuzzy ARTMAP. Over-training in Fuzzy ARTMAP manifests itself in two different ways: (a) it degrades the generalization performance of Fuzzy ARTMAP as training progresses, and (b) it creates unnecessarily large Fuzzy ARTMAP neural network architectures. In this work we are demonstrating that overtraining happens in Fuzzy ARTMAP and we p...

In this paper we introduce a procedure, based on the max-min clustering method, that identifies a fixed order of training pattern presentation for fuzzy adaptive resonance theory mapping (ARTMAP). This procedure is referred to as the ordering algorithm, and the combination of this procedure with fuzzy ARTMAP is referred to as ordered fuzzy ARTMAP. Experimental results demonstrate that ordered f...

In this paper we introduce a procedure, based on the Max-Min clustering method, that identiies a xed order of training pattern presentation for Fuzzy ARTMAP. This procedure is referred to as the Ordering Algorithm, and the combination of this procedure with Fuzzy ARTMAP is referred to as Ordered Fuzzy ARTMAP. Experimental results demonstrate that Ordered Fuzzy ARTMAP exhibits a generalization p...

The various applications of VLSI circuits in highperformance computing, telecommunications, and consumer electronics has been expanding progressively, and at a very hasty pace. This paper describes a new model for partitioning a circuit using DBSCAN and fuzzy ARTMAP neural network. The first step is concerned with feature extraction, where we had make use DBSCAN algorithm. The second step is th...

A fuzzy ARTMAP system is a system for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequence of analog or binary input vectors. The original fuzzy ARTMAP system incorporates two fuzzy ART modules and an inter-ART module. Many different approaches have been proposed to modify fuzzy ARTMAP systems. In this paper, we proposed a new app...

ARTMAP neural network cla.c=;sifiers arc considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes \vith one or more prototypes, perform on-line incremental learning to account for novelty encountered in the field, and process radar pulse streams at high speed, making them attractive for real-time applicat...

In this paper, several modifications to the Fuzzy ARTMAP neural network architecture are proposed for conducting classification in complex, possibly noisy, environments. The goal of these modifications is to improve upon the generalization performance of Fuzzy ART-based neural networks, such as Fuzzy ARTMAP, in these situations. One of the major difficulties of employing Fuzzy ARTMAP on such le...

This paper describes an approach to classification of noisy signals using a technique based on the fuzzy ARTMAP neural network (FAMNN). The proposed method is a modification of the testing phase of the fuzzy ARTMAP that exhibits superior generalization performance compared to the generalization performance of the standard fuzzy ARTMAP in the presence of noise. An application to textured gray-sc...

The Fuzzy ARTMAP algorithm has been proven to be one of the premier neural network architectures for classification problems. One of the properties of Fuzzy ARTMAP, which can be both an asset and a liability, is its capacity to produce new nodes (templates) on demand to represent classification categories. This property allows Fuzzy ARTMAP to automatically adapt to the database without having t...