ART 1

An Adaptive Resonance Theory (ART) neural network architecture accepting binary inputs.


Articles & Tech Transfers


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 ...

Manufacturing cell formation with production data using neural networks
Abstract Batch type production strategies need adoption of cellular manufacturing (CM) in order to improve operational effectiveness by reducing manufacturing lead time and costs related to inventory and material handling. CM ...

Discovery of hierarchical thematic structure in text collections with adaptive resonance theory
Abstract This paper investigates the abilities of adaptive resonance theory (ART) neural networks as miners of hierarchical thematic structure in text collections. We present experimental results with binary ART1 on the benchmark ...

Soft-clustering and improved stability for adaptive resonance theory neural networks
Abstract Stability and plasticity in learning systems are both equally essential, but achieving stability and plasticity simultaneously is difficult. Adaptive resonance theory (ART) neural networks are known for their plastic and ...

Automating construction of a domain ontology using a projective adaptive resonance theory neural network and Bayesian network
Abstract Research on semantic webs has become increasingly widespread in the computer science community. The core technology of a semantic web is an artefact called an ontology. The major problem in constructing an ontology is the ...

Incremental clustering of mixed data based on distance hierarchy
Abstract Clustering is an important function in data mining. Its typical application includes the analysis of consumer s materials. Adaptive resonance theory network (ART) is very popular in the unsupervised neural network. Type I ...

CIRCUIT SIMULATION OF ADAPTIVE RESONANCE THEORY (ART) NEURAL NETWORK USING PSPICE
Abstract This paper outlines the design and simulation of an analogue integrated circuit for the adaptive resonanace theory (ART1) neural network. The circuit is designed based on a set of coupled differential equations which ...

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 ...

A NEURAL-NETWORK-BASED CELL-FORMATION ALGORITHM IN CELLULAR MANUFACTURING
Abstract The Adaptive Resonance Theory (ART) neural network is a novel method for the cell formation problem in group technology (GT). The advantages of using an ART network over other conventional methods are its fast computation ...

INTELLIGENT DESIGN RETRIEVAL AND PACKAGING SYSTEM - APPLICATION OF NEURAL NETWORKS IN DESIGN AND MANUFACTURING
Abstract We describe a hybrid intelligent design retrieval and packaging system by utilizing techniques such as fuzzy associative memory, backpropagation neural networks, and adaptive resonance theory. As an illustrative example, a ...

MACHINE-PART FAMILY FORMATION WITH THE ADAPTIVE RESONANCE THEORY PARADIGM
Abstract The ART1 neural network paradigm employs a heuristic where new vectors are compared with group representative vectors for classification. ART1 is adapted for the cell formation problem by reordering input vectors and by ...

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 ...

On-line recognition of cursive Korean characters using neural networks
Abstract This paper proposes an efficient method for on-line recognition of cursive Korean characters. Since Korean characters are composed of two or three graphemes in two dimensions, strokes, primitive components of the characters, ...

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) ...

A modified ART 1 algorithm more suitable for VLSI implementations
Abstract This paper presents a modification to the original ART 1 algorithm (Carpenter & Grossberg, 1987a, A massively parallel architecture for a self-organizing neural pattern recognition machine, Computer Vision, Graphics, and ...

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 ...

A deployed engineering design retrieval system using neural networks
Abstract We describe a neural information retrieval system (NIRS), now in production within the Boeing Company, which has been developed for the identification and retrieval of engineering designs. Two-dimensional and ...

A neural-network approach to recognize defect spatial pattern in semiconductor fabrication
Abstract Yield enhancement in semiconductor fabrication is important. Even though IC yield loss may be attributed to many problems, the existence of defects on the wafer is one of the main causes. When the defects on the wafer form ...

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) ...

Wafer bin map recognition using a neural network approach
Abstract Although the fabrication of modern integrated circuits uses highly automatic and precisely controlled operations, equipment malfunctions or process drifts are still inevitable owing to the high complexity involved in the ...

On the quality of ART1 text clustering
Abstract There is a large and continually growing quantity of electronic text available, which contain essential human and organization knowledge. An important research endeavor is to study and develop better ways to access this ...

Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis
Abstract This paper presents a new approach for integrating case-based reasoning (CBR) with an ART-Kohonen neural network (ART-KNN) to enhance fault diagnosis. When solving a new problem, the neural network is used to make hypotheses ...

Genetic neuro-nester
Abstract In this paper, the integration of artificial neural networks and genetic algorithms is explored for solving uncured composite stock cutting problem, which is an NP-complete problem. The input patterns can be either ...

Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks
Abstract Benthic macroinvertebrate communities in stream ecosystems were assessed hierarchically through two-level classification methods of unsupervised learning. Two artificial neural networks were implemented in combination. ...

Application of ART neural network to development of technology for functional feature-based reference design retrieval
Abstract Engineering design is a knowledge intensive process. The execution of each task in the process requires various aspects of knowledge and experience. Therefore, organizing, storing and retrieving product design information, ...

Manufacturing cell formation using modified ART1 networks
Abstract The primary objective of group technology (GT) is to enhance the productivity in the batch manufacturing environment. The GT cell formation problem is solved using modified binary adaptive resonance theory networks known as ...

Integrated clustering approach to developing technology for functional feature and engineering specification-based reference design retrieval
Abstract Engineering design is a complex activity, and is heavily reliant on the know-how of engineering designers. Hence, capturing, storing, and reusing design information, design intent, and underlining design knowledge to support ...

RT-UNNID: A practical solution to real-time network-based intrusion detection using unsupervised neural networks
Abstract With the growing rate of network attacks, intelligent methods for detecting new attacks have attracted increasing interest. The RT-UNNID system, introduced in this paper, is one such system, capable of intelligent real-time ...

Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing
Abstract Semiconductor manufacturing involves lengthy and complex processes, and hence is capital intensive. Companies compete with each other by continuously employing new technologies, increasing yield, and reducing costs. Yield ...

A novel approach for vector quantization using a neural network, mean shift, and principal component analysis-based seed re-initialization
Abstract In this paper, a hybrid approach for vector quantization (VQ) is proposed for obtaining the better codebook. It is modified and improved based on the centroid neural network adaptive resonance theory (CNN-ART) and the ...

Fractional cell formation in group technology using modified ART1 neural networks
Abstract Group technology (GT) is a manufacturing philosophy that attempts to reduce production cost by reducing the material handling and transportation cost. The GT cell formation by any known algorithm/heuristics results in much ...

The role of attention in the tinnitus decompensation: reinforcement of a large-scale neural decompensation measure
Abstract Large-scale neural correlates of the tinnitus decompensation have been identified by using wavelet phase stability criteria of single sweep sequences of auditory late responses (ALRs). The suggested measure provided an ...

Proposal of new gene filtering method, BagPART, for gene expression analysis with small sample
Abstract A significant problem in gene expression analysis is that the sample size is substantially lower than the number of genes. Bagging is an effective method of solving this problem in the case of small sample datasets. We have ...

Extensions of vector quantization for incremental clustering
Abstract In this paper, we extend the conventional vector quantization by incorporating a vigilance parameter, which steers the tradeoff between plasticity and stability during incremental online learning. This is motivated in the ...

ART-KOHONEN neural network for fault diagnosis of rotating machinery
Abstract In this paper, a new neural network (NN) for fault diagnosis of rotating machinery which synthesizes the theory of adaptive resonance theory (ART) and the learning strategy of Kohonen neural network (KNN), is proposed. For ...

Hybrid optoelectronic adaptive resonance theory neural processor, ART1
Abstract For industrial use, adaptive resonance theory (ART) neural networks have the potential of becoming an important component in a variety of commercial and military systems. Efficient software emulations of these networks are ...

A dynamical adaptive resonance architecture
Abstract A set of nonlinear differential equations that describe the dynamics of the ART1 model are presented, along with the motivation for their use. These equations are extensions of those developed by Carpenter and Grossberg ...

An optoelectronic implementation of the adaptive resonance neural network
Abstract A solution to the problem of implementation of the adaptive resonance theory (ART) of neural networks that uses an optical correlator which allows the large body of correlator research to be leveraged in the implementation ...

Adaptive resonance theory microchips
Abstract Recently, a real-time clustering microchip based on the ART1 algorithm has been reported. That chip was able to classify 100-bit input patterns into up to 18 categories. However, its high area comsumption (lcm 2) caused a ...

Image processing in HSI color space using adaptive noise filtering
Abstract Adaptive noise filtering is applied to an image frame of HSI data to reduce and more uniformly distribute noise while preserving image feature edges. An adaptive spatial filter includes a plurality of averaging kernels. An ...

Use of generic classifiers to determine physical topology in heterogeneous networking environments
Abstract Round trip time, bottleneck link speed, and hop count information from one node to the remaining nodes within a network is collected and processed by an adaptive resonance theory (ART) neural network to classify the nodes by ...

Hierarchical pattern recognition system with variable selection weights
Abstract In a pattern recognition system, input signals are applied to a short term feature representation field of nodes. A pattern from the short term feature representation field selects at least one category node in a category ...

Use of adaptive resonance theory (ART) neural networks to compute bottleneck link speed in heterogeneous networking environments
Abstract Bottleneck link speed, or the transmission speed of the slowest link within a path between two nodes, is determining by transmitting a sequence of ICMP ECHO data packets from the source node to the target node at a selected ...

Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks
Abstract The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-complete, and is an often-used benchmark for new optimization techniques. One of the ...

Adaptive neural network clustering of web users.
Abstract A neural network based on adaptive resonance theory dynamically groups users based on their Web access patterns. A prefetching application of this clustering technique showed prediction accuracy as high as 97.78 percent. ...

Probing cognitive processes through the structure of event-related potentials during learning: An experimental and theoretical analysis
Abstract Data reporting correlated changes, due to learning, in the amplitudes and chronometry of several eventrelated potentials (ERPs) are compared to neural explanations and predictions of the adaptive resonance theory. The ERP ...

Competitive learning: From interactive activation to adaptive resonance
Abstract Functional and mechanistic comparisons are made between several network models of cognitive processing: competitive learning, interactive activation, adaptive resonance, and back propagation. The starting point of this ...

Predictive regulation of associative learning in a neural network by reinforcement and attentive feedback
Abstract A real time neural network model is described in which reinforcement helps to focus attention upon and organize learning of those environmental events and contingencies that have predicted behavioral success in the past. ...

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 ...

ART and ARTMAP neural networks for applications: Self-organizing learning, recognition, and prediction
Abstract ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing ...

Distributed activation, search, and learning 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 ...

A distributed outstar network for spatial pattern learning
Abstract The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals for a source node cause weights to learn and recall arbitrary patterns across a ...

Distributed outstar learning and the rules of synaptic transmission
Abstract The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals for a source node cause weights to learn and recall arbitrary patterns across a ...

Distributed hypothesis testing, attention shifts, and transmitter dynamics during the self-organization of brain recognition codes
Abstract How the mammalian brain can rapidly but stably learn about a changing world filled with unexpected events is one of the most challenging scientific problems of our time. The brain?s ability to autonomously discover and ...

Neural dynamics of category learning and recognition: Structural invariants, evoked potentials, and reinforcement
Abstract This chapter describes how cognitive recognition codes can be learned in response to a temporal stream of input patterns. This self-organizing learning process automatically buffers, or self-stabilizes, its learning against ...

ART: Self-organizing neural networks for learning and memory of cognitive recognition codes
Abstract Adaptive resonance (ART) architectures are neural networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequences of analog or binary input patterns. In ART architectures, ...

Search mechanisms for Adaptive Resonance Theory (ART) architectures
Abstract A model to implement search in neural network hierarchies is outlined. The system models elementary properties of the chemical synapse, such as transmitter accumulation, depletion, and modulation. The search mechanism is ...

Neural dynamics of category learning and recognition: Attention, memory consolidation, and amnesia
Abstract A theory is developed of how recognition categories can be learned in response to a temporal stream of input patterns. Interactions between an attentional subsystem and an orienting subsystem enable the network to ...

Nonlinear neural networks: Principles, mechanisms, and architectures
Abstract An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter ...

The ART of adaptive pattern recognition by a self organizing neural network
Abstract The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to ...

A massively parallel architecture for a self organizing neural pattern recognition machine
Abstract A neural network architecture for the learning of recognition categories is derived. Real-time network dynamics are completely characterized through mathematical analysis and computer simulations. The architecture ...

Invariant pattern recognition and recall by an attentive ART architecture in a nonstationary world
Abstract A neural network is described which can stably self-organize an invariant pattern recognition code in response to a sequence of analog or digital input patterns; be attentionally p[rimed to ignore all but a designated ...

Fuzzy ART
Abstract Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART by ...

Adaptive Resonance Theory
Abstract Principles derived from an analysis of experimental literatures in vision, speech, cortical development, and reinforcement learning, including attentional blocking and cognitive-emotional interactions, led to the ...

A Neural Network Structure for Detecting Straight Line Segments
Abstract A new method for detecting one-pixel wide vertical, horizontal and diagonal line segments in binary images, is presented. It is based on using four slabs of neural network each of which is composed of a set layers. Each ...

Resonant neural dynamics of speech perception
Abstract What is the neural representation of a speech code as it evolves in time? How do listeners integrate temporally distributed phonemic information across hundreds of milliseconds, even backwards in time, into coherent ...