Dual Vigilance Hypersphere Adaptive Resonance Theory

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.

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

In this paper, we present an anomaly detection system that is able to detect time-related anomalies by using a wireless sensor network and a mobile robot. The sensor network uses an unsupervised fuzzy adaptive resonance theory (ART) neural network to learn and detect intruders in a previously unknown environment. Upon the detection of an intruder, a mobile robot travels to the position where the intruder is detected to investigate by using its camera. The wireless sensor network uses a hierarchical communication/learning structure, where the mobile robot is the root node of the tree. Our fuzzy ART network is based on Kulakov and Davcevpsilas implementation (Kulakov and Davcev, 2005). However, we enhance their work by extending the fuzzy ART neural network with a Markov model to learn a time series and detect time-related anomalies. Finally, a mobile robot is employed to verify whether the detected anomalies were caused by intruders. The proposed architecture is tested on physical hardware. Our results show that our enhanced detection system with mobile robot verification has a higher accuracy and lower false alarm rate than the original fuzzy ART system.

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

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.

Intelligent agents are expected to play an increasingly important role in Supply Chain Management (SCM) by automating event-tracking, trend-prediction and decision-making functions. In this paper, we proposed a new trend-prediction methodology that recognises behavioural patterns and predicts future performance based on those patterns. We used fuzzy Adaptive Resonance Theory (ART) Neural Networks (NNs) to build the patterns and BackPropagation NNs (BPNNs) to make the predictions. We based this methodology on System Dynamics (SD) models, which were used to train the NNs. We believe that our approach could be incorporated easily into a number of software agents. These agents could improve dramatically the capabilities of current dashboard-monitoring systems.

A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extrac- tion based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ''low- level'' features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ''cognitive sys- tem.'' The novelty of the model developed in this paper lies in the use of a self-organizing input layer to the fuzzy ART. Evaluation of the model is performed by using natural textures, and results ob- tained show that the developed model is capable of performing the texture recognition task effectively. Applications of the developed model include the study of artificial vision systems motivated by the human visual system model. © 1997 SPIE and IS&T. (S1017-9909(97)00403-0)

Document clustering has been an important research area in recent years. However, most work on this subject has focused on batch processing in a static environment. For real world applications, online and incremental processing of highly dynamic data is required. Adaptive resonance theory (ART) neural networks possess several interesting properties that make them appealing as a potential solution to this problem. In this paper, we present preliminary experimental results that examine ART text clustering under several situations characteristic of real-life applications. We also compare our present results with work we have conducted previously on the batch static case, hence determining how clustering quality is affected by incremental processing.

Dual vigilance fuzzy adaptive resonance theory

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 case retrieval model based on neural network is presented to enhance the efficiency and quality of retrieving case in the case-based reasoning system of the motorcycle intelligent design. In the retrieval model, the adaptive resonance theory neural network was used to dynamically cluster the cases in the case base to narrow the searching range. The back propagation neural network was applied to memory the index of cases to retrieve quickly the similar case from the narrowed case base. Thus the efficiency and quality of retrieving case are improved. Finally, an example of the plan selection of motorcycle general design was given. Its result was contrasted with that of case retrieval based on the nearest neighbor method to demonstrate the effectives of the case retrieval model. The research shows that it is practicable and effective using the adaptive resonance theory and BP neural network to modeling the reasoning mechanism.

With a focus on classification problem, in this paper, we present an integrated approach to improve the performance of classification using adaptive resonance theory (ART) neural network and logistic regression classifiers. In our approach, the neural network classifier is trained first and then regression analysis is applied to each individual class. In testing phase, the data is applied to the regression classifier and, if any deviation exists, the neural network classifier is retrained. The study reveals that effective data mining can be achieved by combining the power of neural networks with the rigor of more traditional statistical tools.

A digital VLSI circuit design for an adaptive resonance theory (ART) neural network architecture, called the augmented ART-1 neural network (AARTI-NN) is presented. An ‘axon-synapse-tree’ structure is used to realize the activities of the short-term memories and reset subsystem. The long-term memory traces are implemented using NMOS transmission gales. PSpice circuit simulation was carried out to verify the design of a prototype, seven-node AARTI-NN. A clock-controlled delay element is included in the simulation to illustrate the functionality of the AARTI-NN. It is shown that the AARTI-NN node selection activities simulated from the circuit designed are identical to those described by the coupled differential equations governing the AARTI-NN.

Self-organizing large amounts of textual data in accordance to some topics structure is an increasingly important application of clustering. Adaptive resonance theory (ART) neural networks possess several interesting properties that make them appealing in this area. Although ART has been used in several research works as a text clustering tool, the level of quality of the resulting document clusters has not been clearly established yet. In this paper, we present experimental results with binary ART that address this issue by determining how close clustering quality is to an upper bound on clustering quality.

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.