A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

ART Neural Networks: Distributed Coding and ARTMAP Applications

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

The category proliferation problem in ART neural networks

This article describes the design of a new model IKMART, for classification of documents and their incorporation into categories based on the KMART architecture. The architecture consists of two networks that mutually cooperate through the interconnection of weights and the output matrix of the coded documents. The architecture retains required network features such as incremental learning without the need of descriptive and input/output fuzzy data, learning acceleration and classification of documents and a minimal number of user-defined parameters. The conducted experiments with real documents showed a more precise categorization of documents and higher classification performance in comparison to the classic KMART algorithm.Web of Science145634

Adaptive Resonance: An Emerging Neural Theory of Cognition

Adaptive resonance is a theory of cognitive information processing which has been realized as a family of neural network models. In recent years, these models have evolved to incorporate new capabilities in the cognitive, neural, computational, and technological domains. Minimal models provide a conceptual framework, for formulating questions about the nature of cognition; an architectural framework, for mapping cognitive functions to cortical regions; a semantic framework, for precisely defining terms; and a computational framework, for testing hypotheses. These systems are here exemplified by the distributed ART (dART) model, which generalizes localist ART systems to allow arbitrarily distributed code representations, while retaining basic capabilities such as stable fast learning and scalability. Since each component is placed in the context of a unified real-time system, analysis can move from the level of neural processes, including learning laws and rules of synaptic transmission, to cognitive processes, including attention and consciousness. Local design is driven by global functional constraints, with each network synthesizing a dynamic balance of opposing tendencies. The self-contained working ART and dART models can also be transferred to technology, in areas that include remote sensing, sensor fusion, and content-addressable information retrieval from large databases.Office of Naval Research and the defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (20-316-4304-5

A Neural Network Architecture for Autonomous Learning, Recognition, and Prediction in a Nonstationary World

In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and narne novel objects without unselectively disrupting our memories of familiar ones. We can notice fine details that differentiate nearly identical objects and generalize across broad classes of dissimilar objects. This chapter describes a class of self-organizing neural network architectures--called ARTMAP-- that are capable of fast, yet stable, on-line recognition learning, hypothesis testing, and naming in response to an arbitrary stream of input patterns (Carpenter, Grossberg, Markuzon, Reynolds, and Rosen, 1992; Carpenter, Grossberg, and Reynolds, 1991). The intrinsic stability of ARTMAP allows the system to learn incrementally for an unlimited period of time. System stability properties can be traced to the structure of its learned memories, which encode clusters of attended features into its recognition categories, rather than slow averages of category inputs. The level of detail in the learned attentional focus is determined moment-by-moment, depending on predictive success: an error due to over-generalization automatically focuses attention on additional input details enough of which are learned in a new recognition category so that the predictive error will not be repeated. An ARTMAP system creates an evolving map between a variable number of learned categories that compress one feature space (e.g., visual features) to learned categories of another feature space (e.g., auditory features). Input vectors can be either binary or analog. Computational properties of the networks enable them to perform significantly better in benchmark studies than alternative machine learning, genetic algorithm, or neural network models. Some of the critical problems that challenge and constrain any such autonomous learning system will next be illustrated. Design principles that work together to solve these problems are then outlined. These principles are realized in the ARTMAP architecture, which is specified as an algorithm. Finally, ARTMAP dynamics are illustrated by means of a series of benchmark simulations.Advanced Research Projects Agency (N00014-92-J-4015); British Petroleum (89A-1204); National Science Foundation (IRI-90-J-4015); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

An ARTMAP-incorporated Multi-Agent System for Building Intelligent Heat Management

This paper presents an ARTMAP-incorporated multi-agent system (MAS) for building heat management, which aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control). Existing MAS typically uses rule-based approaches to describe the behaviours and the processes of its agents, and the rules are fixed. The incorporation of artificial neural network (ANN) techniques to the agents can provide for the required online learning and adaptation capabilities. A three-layer MAS is proposed for building heat management and ARTMAP is incorporated into the agents so as to facilitate online learning and adaptation capabilities. Simulation results demonstrate that ARTMAP incorporated MAS provides better (automated) energy control and thermal comfort management for a building environment in comparison to its existing rule-based MAS approach

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Genetically Engineered Adaptive Resonance Theory (art) Neural Network Architectures

Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this phenomenon. In this thesis we propose a new approach to handle the category proliferation problem in Fuzzy ARTMAP by evolving trained FAM architectures. We refer to the resulting FAM architectures as GFAM. We demonstrate through extensive experimentation that an evolved FAM (GFAM) exhibits good (sometimes optimal) generalization, small size (sometimes optimal size), and requires reasonable computational effort to produce an optimal or sub-optimal network. Furthermore, comparisons of the GFAM with other approaches, proposed in the literature, which address the FAM category proliferation problem, illustrate that the GFAM has a number of advantages (i.e. produces smaller or equal size architectures, of better or as good generalization, with reduced computational complexity). Furthermore, in this dissertation we have extended the approach used with Fuzzy ARTMAP to other ART architectures, such as Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM) that also suffer from the ART category proliferation problem. Thus, we have designed and experimented with genetically engineered EAM and GAM architectures, named GEAM and GGAM. Comparisons of GEAM and GGAM with other ART architectures that were introduced in the ART literature, addressing the category proliferation problem, illustrate similar advantages observed by GFAM (i.e, GEAM and GGAM produce smaller size ART architectures, of better or improved generalization, with reduced computational complexity). Moverover, to optimally cover the input space of a problem, we proposed a genetically engineered ART architecture that combines the category structures of two different ART networks, FAM and EAM. We named this architecture UART (Universal ART). We analyzed the order of search in UART, that is the order according to which a FAM category or an EAM category is accessed in UART. This analysis allowed us to better understand UART\u27s functionality. Experiments were also conducted to compare UART with other ART architectures, in a similar fashion as GFAM and GEAM were compared. Similar conclusions were drawn from this comparison, as in the comparison of GFAM and GEAM with other ART architectures. Finally, we analyzed the computational complexity of the genetically engineered ART architectures and we compared it with the computational complexity of other ART architectures, introduced into the literature. This analytical comparison verified our claim that the genetically engineered ART architectures produce better generalization and smaller sizes ART structures, at reduced computational complexity, compared to other ART approaches. In review, a methodology was introduced of how to combine the answers (categories) of ART architectures, using genetic algorithms. This methodology was successfully applied to FAM, EAM and FAM and EAM ART architectures, with success, resulting in ART neural networks which outperformed other ART architectures, previously introduced into the literature, and quite often produced ART architectures that attained optimal classification results, at reduced computational complexity

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624