Modified ART

ART variants and modifications apart from the ones mentioned above.


Articles & Tech Transfers


dFasArt: Dynamic neural processing in FasArt model
Abstract The temporal character of the input is, generally, not taken into account in the neural models. This paper presents an extension of the FasArt model focused on the treatment of temporal signals. FasArt model is proposed as ...

A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier
Abstract Based on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct ...

AG-ART: An adaptive approach to evolving ART architectures
Abstract This paper focuses on classification problems, and in particular on the evolution of ARTMAP architectures using genetic algorithms, with the objective of improving generalization performance and alleviating the adaptive ...

Coordinated machine learning and decision support for situation awareness
Abstract Domains such as force protection require an effective decision maker to maintain a high level of situation awareness. A system that combines humans with neural networks is a desirable approach. Furthermore, it is ...

Projective ART with buffers for the high dimensional space clustering and an application to discover stock associations
Abstract Unlike to traditional hierarchical and partitional clustering algorithms which always fail to deal with very large databases, a neural network architecture, projective adaptive resonance theory (PART), is developed for the ...

View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds
Abstract How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are ...

Robust Modular Artmap For Multi-Class Shape Recognition
Abstract This paper presents a Fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as Modular Adaptive Resonance Theory Map (MARTMAP). The prediction of class membership is made collectively by ...

Photorefractive Adaptive Resonance Neural Network
Abstract We describe a novel adaptive resonance theory (ART) device that is fully optical in the input-output processing path. This device is based on holographic information processing in a photorefractive crystal. This sets up an ...

ANALOG CIRCUIT-DESIGN AND IMPLEMENTATION OF AN ADAPTIVE RESONANCE THEORY (ART) NEURAL-NETWORK ARCHITECTURE
Abstract An analogue circuit implementation is presented for an adaptive resonance theory neural network architecture, called the augmented ART-1 neural network (AARTI-NN). The AARTI-NN is a modification of the popular ARTI-NN, ...

AN INVARIANT PATTERN-RECOGNITION MACHINE USING A MODIFIED ART ARCHITECTURE
Abstract A novel invariant pattern recognition machine is proposed based on a modified ART architecture. Invariance is achieved by adding a new layer called F23, beyond the F2 layer in the ART architecture. The design of the weight ...

DIGITAL VLSI CIRCUIT-DESIGN AND SIMULATION OF AN ADAPTIVE RESONANCE THEORY NEURAL-NETWORK
Abstract A digital VLSI circuit design for an adaptive resonance theory (ART) neural network architecture, called the augmented ART-1 neural network (AART1-NN) is presented. An axon-synapse-tree structure is used to realize the ...

DETECTING PROCESS NONRANDOMNESS THROUGH A FAST AND CUMULATIVE LEARNING ART-BASED PATTERN RECOGNIZER
Abstract An adaptive resonance theory (ART) based, general-purpose control chart pattern recognizer (CCPR) which is capable of fast and cumulative learning is presented. The implementation of this ART-based CCPR was made possible by ...

A modified fuzzy ARTMAP architecture for the approximation of noisy mappings
Abstract A neural architecture, fuzzy ARTMAP, is considered here as an alternative to standard feedforward networks for noisy mapping tasks. It is one of a series of architectures based upon adaptive resonance theory or ART. Like ...

Gaussian ARTMAP: A neural network for past incremental learning of noisy multidimensional maps
Abstract A new neural network architecture for incremental supervised learning of analog multidimensional maps is introduced. The architecture, called Gaussian ARTMAP, is a synthesis of a Gaussian classifier and an adaptive resonance ...

An ART-based modular architecture for learning hierarchical clusterings
Abstract This paper introduces a neural architecture (HART for Hierarchical ART ) that is capable of learning hierarchical clusterings of arbitrary input sequences, The network is built up of layers of Adaptive Resonance Theory (ART) ...

An analysis of Kansei structure on shoes using self-organizing neural networks
Abstract Kansei engineering is a technology for translating human feelings into product design. Several multivariate analyses are used for analyzing human feelings and building rules. Although these methods are reliable, they require ...

Acquiring rule sets as a product of learning in a logical neural architecture
Abstract Envisioning neural networks as systems that learn rules calls forth the verification issues already being studied in knowledge-based systems engineering, and complicates these with neural-network concepts such as nonlinear ...

Exact ART: A complete implementation of an ART network
Abstract In this article we introduce a continuous time implementation of adaptive resonance theory (ART). ART designed by Grossberg concerns neural networks that self-organize stable pattern recognition categories of arbitrary ...

Modular mART for 3D target recognition
Abstract A modified adaptive resonance theory (mART) neural network of modular structure is proposed. The similarity function and weight resolution of the ART neural networks are modified, and the cluster merging algorithm and ...

Using neural networks for the diagnosis of localized defects in ball bearings
Abstract Two neural network based approaches, a multilayered feed forward neural network trained with supervised Error Back Propagation technique and an unsupervised Adaptive Resonance Theory-2 (ART2) based neural network were used ...

Application of wavelets and neural networks to diagnostic system development, 1, feature extraction
Abstract An integrated framework for process monitoring and diagnosis is presented which combines wavelets for feature extraction from dynamic transient signals and an unsupervised neural network for identification of operational ...

Properties of learning of a Fuzzy ART Variant
Abstract This paper discusses a variation of the Fuzzy ART algorithm referred to as the Fuzzy ART Variant. The Fuzzy ART Variant is a Fuzzy ART algorithm that uses a very large choice parameter value. Based on the geometrical ...

Classification of malignant and benign masses based on hybrid ART2LDA approach
Abstract A new type of classifier combining an unsupervised and a supervised model was designed and applied to classification of malignant and benign masses on mammograms. The unsupervised model was based on an adaptive resonance ...

Fuzzy lattice neurocomputing (FLN) models
Abstract In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the capacity to deal rigorously with: disparate ...

Learning from noisy information in FasArt and FasBack neuro-fuzzy systems
Abstract Neuro-fuzzy systems have been in the focus of recent research as a solution to jointly exploit the main features of fuzzy logic systems and neural networks. Within the application literature, neuro-fuzzy systems can be found ...

A Modified Fuzzy ART for Soft Document Clustering
Abstract Document clustering is a very useful application in recent days especially with the advent of the World Wide Web. Most of the existing document clustering algorithms either produce clusters of poor quality or are highly ...

A novel approach to the derivation of fuzzy membership functions using the Falcon-MART architecture
Abstract A fuzzy neural network, Falcon-MART, is proposed in this paper. This is a modification of the original Falcon-ART architecture. Both Falcon-ART and Falcon-MART are fuzzy neural networks that can be used as fuzzy controllers ...

NeuroFAST: On-line neuro-fuzzy ART-based structure and parameter learning TSK model
Abstract NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high function approximation accuracy and fast convergence. It is based on a first-order Takagi-Sugeno-Kang (TSK) model, where the consequence part of each ...

Projective ART for clustering data sets in high dimensional spaces
Abstract A new neural network architecture (PART) and the resulting algorithm are proposed to find projected clusters for data sets in high dimensional spaces. The architecture is based on the well known ART developed by Carpenter ...

Constructive feedforward ART clustering networks - Part I
Abstract Part I of this paper proposes a definition of the adaptive resonance theory (ART) class of constructive unsupervised on-line learning clustering networks. Class ART generalizes several well-known clustering models, e.g., ART ...

Constructive feedforward ART clustering networks - Part II
Abstract Part I of this paper defines the class of constructive unsupervised on-line learning simplified adaptive resonance theory (SART) clustering networks. Proposed instances of class SART are the symmetric Fuzzy ART (S-Fuzzy ART) ...

Automatic change detection of driving environments in a vision-based driver assistance system
Abstract Detecting critical changes of environments while driving is an important task in driver assistance systems. In this paper, a computational model motivated by human cognitive processing and selective attention is proposed for ...

Puzzle-solving science: the quixotic quest for units in speech perception
Abstract Although speech signals are continuous and variable, listeners experience segmentation and linguistic structure in perception. For years, researchers have tried to identify the basic building-block of speech perception. In ...

Learning user profiles for personalized information dissemination
Abstract Personalized information systems represent the recent effort of delivering information to users more effectively in the modern electronic age. This paper illustrates how a supervised adaptive resonance theory (ART) system, ...

Supervised adaptive resonance theory neural network for modelling dynamic systems
Abstract A supervised neural network, SMART2, has been developed which can be used with the ART2 algorithm for modelling discrete dynamic systems. A new layer has been added as a higher transformation stage to provide an output ...

A Gaussian adaptive resonance theory neural network classificationalgorithm applied to supervised land cover mapping using multitemporalvegetation index data
Abstract Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the ...

Adaptive resonance neural classifier for identification of gases/odours using an integrated sensor array
Abstract A new approach to intelligent gas sensor (IGS) design using a classifier based on adaptive resonance theory (ART) artificial neural network (ANN) is presented. Using published data of sensor arrays fabricated and ...

Centroid neural network adaptive resonance theory for vector quantization
Abstract In this paper, a novel unsupervised competitive learning algorithm, called the centroid neural network adaptive resonance theory (CNN-ART) algorithm, is proposed to relieve the dependence on the initial codewords of the ...

Supervised training of a neural network
Abstract The present invention provides a system and method for supervised training of a neural network. A neural network architecture and training method is disclosed that is a modification of an ARTMAP architecture. The modified ...

Adaptive vector quantization/quantizer
Abstract An adaptive vector quantization process and quantizer (VQ) using a clustering technique known as AFLC (adaptive fuzzy leader clustering) is disclosed. The quantizer, AFLC-VQ, has been designed to vector quantize wavelet ...

Concept Hierarchy Memory Model: A neural architecture for conceptual knowledge representation, learning, and commonsense reasoning
Abstract This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a ...

Use of adaptive resonance theory to differentiate network device types (routers vs switches)
Abstract To determine a network communications device type, (switch or router) without reference to internal information within the network communications device, two packets having preselected, differing sizes (e.g., 64 bytes and ...

Cascade ARTMAP: Integrating neural computation and symbolic knowledge processing
Abstract This paper introduces Cascade ARTMAP, a system based on fuzzy ARTMAP with an addition of rule-based knowledge discovery and prior knowledge initialization, used for symbolic knowledge extraction and ...

Learn Sesame - A Learning Agent Engine
Abstract Open Sesame!® 1.0—released in 1993—was the world’s first commercial user interface (UI) learning agent. The development of this agent involved a number of decisions about basic design issues that had not been previously ...

µ-ARTMAP: Use of Mutual Information for Category Reduction in Fuzzy ARTMAP
Abstract A new architecture, called ARTMAP, is proposed to impact a category proliferation problem present in Fuzzy ARTMAP. Under a probabilistic setting, it seeks a partition of the input space that optimizes the mutual information ...

An Ordering Algorithm for Pattern Presentation in Fuzzy ARTMAP That Tends to Improve Generalization Performance
Abstract 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 ...

An Integrated SOM-Fuzzy ARTMAP Neural System for the Evaluation of Toxicity
Abstract Self-organized maps (SOM) have been applied to analyze the similarities of chemical compounds and to select from a given pool of descriptors the smallest and more relevant subset needed to build robust QSAR models based on ...

Fuzzy LAPART Supervised Learning Through Inferencing for Stable Category Recognition
Abstract Fuzzy LAPART (laterally primed adaptive resonance theory), a neural network architecture for supervised learning through logical inferencing, is introduced with fast and slow learning algorithms and match tracking ...

Hypersphere ART and ARTMAP for Unsupervised and Supervised, Incremental Learning
Abstract A novel adaptive resonance theory (ART) neural network architecture is being proposed. The new model, called Hypersphere ART (H-ART) is based on the same principals like Fuzzy-ART does and, thus, inherits most of its ...

Learning and Foraging in Robot-bees
Abstract Honey-bees have long served as a model organism for investigating insect navigation and collective behavior: they exhibit division of labor and are an example of insect societies where direct communication between workers ...

Adaptive resonance associative map
Abstract This article introduces a neural architecture termed Adaptive Resonance Associative Map (ARAM) that extends unsupervised Adaptive Resonance Theory (ART) systems for rapid, yet stable, heteroassociative learning. ARAM can be ...

ART-2 and multiscale ART-2 for on-line process fault detection validation via industrial case study
Abstract Data from most industrial processes contain contributions at multiple scales in time and frequency. In contrast, most existing methods for fault detection are best for detecting events at only one scale. This paper provides ...

A self-organizing neural system for learning to recognize textured scenes
Abstract A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices ...

The link between brain learning, attention, and consciousness
Abstract The processes whereby our brains continue to learn about a changing world in a stable fashion throughout life are proposed to lead to conscious experiences. These processes include the learning of top-down expectations, the ...

The resonant dynamics of speech perception: Interword integration and duration-dependent backward effects
Abstract How do listeners integrate temporally distributed phonemic information into coherent representations of syllables and words? For example, increasing the silence interval between the words "gray chip" may result in the ...

ARTSTREAM: A neural network model of auditory scene analysis and source segregation
Abstract Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an ...

Towards a unified theory of neocortex: Laminar cortical circuits for vision and cognition
Abstract A key goal of computational neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how laminar neocortical circuits give rise to biological ...

Laminar cortical dynamics of 3D surface perception: Stratification, transparency, and neon color spreading
Abstract The 3D LAMINART neural model is developed to explain how the visual cortex gives rise to 3D percepts of stratification, transparency, and neon color spreading in response to 2D pictures and 3D scenes. Such percepts are ...

Neural dynamics of autistic behaviors: Cognitive, emotional, and timing substrates
Abstract What brain mechanisms underlie autism, and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the Imbalanced Spectrally Timed Adaptive Resonance Theory (iSTART) model, that ...

Consciousness CLEARS the mind
Abstract A full understanding of consciousness requires that we identify the brain processes from which conscious experiences emerge. What are these processes, and what is their utility in supporting successful adaptive behaviors? ...

Distributed prediction and hierarchical knowledge discovery by ARTMAP neural networks
Abstract 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 ...

Threshold determination for ARTMAP-FD familiarity discrimination
Abstract The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes ...

Distributed learning, recognition, and prediction by ART and ARTMAP neural networks
Abstract A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning ...

ARTMAP-FD: Familiarity discrimination applied to radar target recognition
Abstract ARTMAP-FD extends fuzzy ARTMAP to perform familiarity discrimination. That is, the network learns to abstain from meaningless guesses on patterns not belonging to a class represented in the training set. ARTMAP-FD can also ...

ART-EMAP: A neural network architecture for object recognition by evidence accumulation
Abstract A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and ...

Rule extraction: From neural architecture to symbolic representation
Abstract This paper shows how knowledge, in the form of fuzzy rules, can be derived from a supervised learning neural network called furry ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network ...

3-D object recognition by the ART EMAP evidence accumulation network
Abstract ART-EMAP synthesizes adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). The network extends the capabilities of fuzzy ARTMAP in four incremental stages. ...

Fusion ART A neural network architecture for multi channel data fusion and classification
Abstract Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi?sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ...

Fusion ART An adaptive fuzzy network for multi channel classification
Abstract Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The ...

Working memories for storage and recall of arbitrary temporal sequences
Abstract Working memory neural networks, called Sustained Temporal Order REcurrent (STORE) models, encode the invariant temporal order of sequential events in short term memory (STM). Inputs to the networks may be presented with ...

Default ARTMAP 2
Abstract Default ARTMAP combines winner-take-all category node activation during training, distributed activation during testing, and a set of default parameter values that define a ready-to-use, general-purpose neural network system ...

Spikes, synchrony, and attentive learning by laminar thalamocortical circuits
Abstract This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and ...

Dynamics of projective adaptive resonance theory model: the foundation of PART algorithm
Abstract Projective adaptive resonance theory (PART) neural network developed by Cao and Wu recently has been shown to be very effective in clustering data sets in high dimensional spaces. The PART algorithm is based on the ...

ARTSCENE: A Neural System for Natural Scene Classification
Abstract How do humans rapidly recognize a scene? How can neural models capture this biological competence to achieve state-of-the-art scene classification? The ARTSCENE neural system classifies natural scene photographs by using ...

ARTMAP-DS: Pattern discrimination by discounting similarities
Abstract ARTMAP-DS extends fuzzy ARTMAP to discriminate between similar inputs by discounting similarities. When two or more candidate category representations are activated by a given input, features that the candidate ...

ARTMAP-FTR: A neural network for fusion target recognition, with application to sonar classification
Abstract ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool ...

ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases
Abstract For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking ...

Combining distributed and localist computations in real-time neural networks
Abstract In order to benefit from the advantages of localist coding neural models that feature winner-take-all representations at the top level of a network hierarchy must still solve the computational problems inherent in ...

Default ARTMAP
Abstract The default ARTMAP algorithm and its parameter values specified here define a ready-to-use general-purpose neural network system for supervised learning and ...

Texture segregation by visual cortex: Perceptual grouping, attention, and learning
Abstract A neural model called dARTEX is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model unifies five interacting processes: region-based texture ...

Linking mind to brain: The mathematics of biological intelligence
Abstract How our brains give rise to our minds is one of the most intriguing questions in all of science. We are now living in a particularly interesting time to consider this question. This is true because, during the last decade, ...

Towards a theory of the laminar architecture of cerebral cortex
Abstract One of the most exciting and open research frontiers in neuroscience is that of seeking to understand the functional roles of the layers of cerebral cortex. New experimental techniques for probing the laminar circuitry of ...


Software


Biased ARTMAP
Description Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved ...