Mapping adaptive resonance theory onto ring and mesh architectures

Photometric clustering of regenerated plants of gladiolus by neural networks and its biological validation

ART-2 is a self-organized and unsupervised artificial neural network constructed from adaptive resonance theory which can be used to classify continuous active data. We have found that the theory is limited of the same phase data with different amplitudes and insensitivity to gradual change data during the simulation of data classified with ART-2 neural network. Therefore, we propose a new neural network model based on adaptive resonance theory. We provide the model construction and relevant algorithm as well as the comparison with ART-2.

While learning algorithms have been used for astronomical data analysis, the vast majority of those algorithms have used supervised learning. In a continuation of the work described in Young et ah [18] we examine the use of unsupervised learning for this task with two types of Adaptive Resonance Theory (ART) neural networks. Using synthetic astronomical data from SkyMaker[2], [3] which was designed to mimic the dynamic range of the CTI-[14] telescope, we compared the ability of the ART-1 neural network[4] and the ART-1 neural network with category theoretic modiflcation[9], [11] to detect regions of interest and to characterize noise. We show a difference in the geometries of the templates created by each architecture. We also show an analysis of the two architectures over a range of parameter settings. The results provided show that ART neural networks and unsupervised learning algorithms in general should not be overlooked for astronomical data analysis.

With the increasing emphasis on customer demands by organizations, customer requirements management has secured a crucial position during the early stage of product conceptualization. However, it is still of scarcity to have a systematic strategy for soliciting customer requirements effectively. For this purpose, a customer-oriented approach has accordingly been proposed in this study. This prototype system is composed of two interrelated components, namely, the customer requirements elicitation (CRE) and customer/marketing analysis (CMA) modules. In the context of the prototype system, the laddering technique has been employed to enable the customer requirements elicitation via the so-called customer attributes hierarchy (CAH). In addition, the adaptive resonance theory (particularly ART2) neural network has also been applied as a toolkit for further customer and marketing analysis. A case study on mobile hand phone design is used as an example to validate and illustrate the prototype system.

The adaptive resonance theory 2 (ART2) neural network exhibits several properties which can be useful in the data mining and which are lacking in most other neural networks. But ART2 has deficiencies that the categories clustered by ART2 are very mutable to slight changes in training conditions. An improved ART2 with enhanced triplex matching mechanism, named as ETM-ART2, is presented to redress the deficiencies. Several tests results show that ETM-ART2 performs better than classic ART2 when applied to clustering tasks. It is an effective improved algorithm and can be applied to a wide variety of problems.

Adaptive Resonance Theory (ART), developed by Stephen Grossberg and Gail Carpenter, has attracted some enthusiasts worldwide as a promising approach of artificial neural networks (ANN) for pattern recognition and classification tasks. However, this technology is still not popular among IT professionals and its adoption for data mining applications has not spread out as one could expect. We believe that one reason for this fact is the lack of knowledge about the ART ANN behavior as the parameters change in space. To explore this idea, we executed almost eight hundred experimental tests to map ART1, ART2 and Fuzzy-ART configuration parameters. Some analyses have been done and the results can help system analysts deal with this kind of ANN and explore its complete power. Experimental data, referring to simulated and real databases, have shown that dependent parameter ~ could be clearly defined in Euclidean space R2 as a function of the vigilance parameter p and other patterns and network parameters in ART1, ART2 and Fuzzy-ART models. These results set numerical boundaries up that are useful for data mining application designers using such technology. For instance, the behavior of normalized parameter < as a function of p shows that it is possible to overcome problems with binary coding in ART1 by imposing higher values of p to get complete featured cluster vectors and attenuate (or eliminate) the “bit flood effect” of “O” and” 1“ observed in “1of-N” or “thermometer” coding. An important conclusion is that ART1, ART2 and Fuzzy-ART neural networks, as many other ANN approaches, can support data mining applications based on unsupervised learning (for on line systems) overcoming the stability-plasticity dilemma present in other ANN models. © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved. Web: www.witpress.com Email witpress@witpress.com Paper from: Data Mining III, A Zanasi, CA Brebbia, NFF Ebecken & P Melli (Editors). ISBN 1-85312-925-9

Automated detection of lane-blocking freeway incidents using artificial neural networks

In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.

An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) &amp; HS-ARTMAP (Hyper-spherical ARTMAP network) neural network

This study presents a simplified fuzzy ARTMAP (SFAM) for different classification applications. The proposed SFAM model is synergy of fuzzy logic and adaptive resonance theory (ART) neural networks. SFAM is supervised network consisting of two layers (Fuzzy ART and Inter ART) that build constant classification groups in answer to series of input patterns. Fuzzy ART layer takes a series of input patterns and relate them to output classes. Inter ART layer functions in such a way that it raises the vigilance parameter of fuzzy ART layer. By combining this two layers, SFAM is capable to perform classification very efficiently and giving very high performances. Lastly, the SFAM model is applied to different simulations. The simulation results obtained for the three different datasets: Iris, Wisconsin breast cancer and wine dataset prove that SFAM model has better performance results than other models for these classification applications.

The internal representation of the categories in Adaptive Resonance Theory (ART) neural networks can greatly affect the quality and compactness of the discovered clusters. Dual vigilance thresholds have been shown to yield a significant improvement in Fuzzy ART performance and allow it to retrieve arbitrarily shaped clusters while maintaining the important advantage of a simple architecture and implementation. In this study, we examine the use of Hypersphere ART within the same dual vigilance architecture, thereby presenting the Dual Vigilance Hypersphere ART (DVHA). We conduct an extensive comparison between 6 different ART-based approaches across a set of 30 benchmark datasets, using two different input ordering methods and 30 repeated runs. We find that DVHA ranks better than Dual Vigilance Fuzzy ART (DVFA) on average for many datasets, both in terms of performance and network compactness. Furthermore, although another multi-category-based architecture showed statistically superior results when the inputs are shuffled, we found no statistical difference in performance when the input was pre-processed using the visual assessment of cluster tendency (VAT), while generally being much simpler to implement and less computationally demanding. These findings make DVHA a viable alternative to its Fuzzy ART counterpart, and a simpler alternative to the other studied multi-category-based approaches in cases where resources are limited, such as embedded and hardware-based applications, provided that the input can be preprocessed using VAT.

Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for technology transfer [4]. A neural computation central to both the scientific and the technological analyses is the ART matching rule [5], which models the interaction between top-down expectation and bottom-up input, thereby creating a focus of attention which, in turn, determines the nature of coded memories. Sites of early and ongoing transfer of ART-based technologies include industrial venues such as the Boeing Corporation [6] and government venues such as MIT Lincoln Laboratory [7]. A recent report on industrial uses of neural networks [8] states: ”[The] Boeing ... Neural Information Retrieval System is probably still the largest-scale manufacturing application of neural networks. It uses [ART] to cluster binary templates of aeroplane parts in a complex hierarchical network that covers over 100,000 items, grouped into thousands of self-organised clusters. Claimed savings in manufacturing costs are in millions of dollars per annum.” At Lincoln Lab, a team led by Waxman developed an image mining system which incorporates several models of vision and recognition developed in the Boston University Department of Cognitive and Neural Systems (BU/CNS). Over the years a dozen CNS graduates (Aguilar, Baloch, Baxter, Bomberger, Cunningham, Fay, Gove, Ivey, Mehanian, Ross, Rubin, Streilein) have contributed to this effort, which is now located at Alphatech, Inc. Customers for BU/CNS neural network technologies have attributed their selection of ART over alternative systems to the model’s defining design principles. In listing the advantages of its THOT(r) technology, for example, American Heuristics Corporation (AHC) cites several characteristic computational capabilities of this family of neural models, including fast on-line (one-pass) learning, “vigilant” detection of novel patterns, retention of rare patterns, improvement with experience, ”weights [which] are understandable in real world terms,” and scalability (www.heuristics.com). Design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks, including fuzzy ARTMAP [9], ART-EMAP [10], ARTMAP-IC [11], Gaussian ARTMAP [12], and distributed ARTMAP [3,13]. Comparative analysis of these systems has led to the identification of a default ARTMAP network, which features simplicity of design and robust performance in many application domains [4,14]. Selection of one particular ARTMAP algorithm is intended to facilitate ongoing technology transfer. The default ARTMAP algorithm outlines a procedure for labeling an arbitrary number of output classes in a supervised learning problem. A critical aspect of this algorithm is the distributed nature of its internal code representation, which produces continuous-valued test set predictions distributed across output classes. The character of their code representations, distributed vs. winner-take-all, is, in fact, a primary factor differentiating various ARTMAP networks. The original models [9,15] employ winner-take-all coding during training and testing, as do many subsequent variations and the majority of ART systems that have been transferred to technology. ARTMAP variants with winner-take-all (WTA) coding and discrete target class predictions have, however, shown consistent deficits in labeling accuracy and post-processing adjustment capabilities. The talk will describe a recent application that relies on distributed code representations to exploit the ARTMAP capacity for one-to-many learning, which has enabled the development of self-organizing expert systems for multi-level object grouping, information fusion, and discovery of hierarchical knowledge structures. A pilot study has demonstrated the network’s ability to infer multi-level fused relationships among groups of objects in an image, without any supervised labeling of these relationships, thereby pointing to new methodologies for self-organizing knowledge discovery.

Distributed memory and localized memory in neural networks

We combine neural networks (NNs) and hidden Markov models (HMMs) techniques in order to obtain the lithology identification of wells situated in the Triasic province (Sahara). For the same aim, two systems based on adaptive resonance theory (ART), ART1 and fuzzy ART, are also developed. Our objective is to facilitate the work of the geological experts by permitting them to obtain quickly the structure and the nature of lands around the drilling. Lithology identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir characterisation. In this paper, we show that it is interesting to combine the respective capacities of the HMMs and NNs to produce a new effective hybrid models that draw their source in the two formalisms and can provide us a more reliable reservoir model. Comparisons are established to show that the results obtained by the NN-HMM hybrid system are close to those obtained by the fuzzy ART approach applied to the same borehole with the same well logs

The feature cerebellar model arithmetic computer (FCMAC) is a multiple-input-single-output neural network which can provide three-degree-of-freedom (3-DOF) pose estimation for a robotic vision system. In this paper a new architecture is introduced which combines the FCMAC with an adaptive resonance theory (ART) network. The ART module clusters patterns observed during training into a set of prototypes that are used to build the FCMAC. As a result, the network no longer grows without bound, but constrains itself to a user-specified size. Pose estimates remain accurate since the ART tends to discard the least relevant information first. In some cases the smaller network is better for generalization, resulting in a reduction of error at recall time. The ART-C algorithm is extended to include initial filling with randomly selected patterns (referred to as ART-F). In experiments using a real-world data set, the new network performed equally well using less than one tenth the number of coarse patterns as a regular FCMAC. In validation experiments, the FCMAC system outperformed radial basis function (RBF) networks for the 3-DOF problem, and had comparable performance to principle component analysis (PCA) which estimates orientation only.