Machine Learning

Algorithms and techniques which allow computers to learn based of various inputs.


Articles & Tech Transfers


An Alternative Evaluation of FMEA: Fuzzy ART Algorithm
Abstract Failure Mode and Effects Analysis (F MEA) is a technique used in the manufacturing industry to improve production quality and productivity. It is a method that evaluates possible failures in the system, design, process or ...

Pattern recognition for sensor array signals using Fuzzy ARTMAP
Abstract A Fuzzy ARTMAP classifier for pattern recognition in chemical sensor array was developed based on Fuzzy Set Theory and Adaptive Resonance Theory. In contrast to most current classifiers with difficulty in detecting new ...

Fault diagnosis of pneumatic systems with artificial neural network algorithms
Abstract Pneumatic systems repeat the identical programmed sequence during their operation. The data was collected when the pneumatic system worked perfectly and had some faults including empty magazine, zero vacuum, inappropriate ...

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 ...

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 ...

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 ...

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 ...

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 ...

Computational Intelligence Meets the NetFlix Prize
Abstract The NetFlix Prize is a research contest that will award $1 Million to the first group to improve NetFlix's movie recommendation system by 10%. Contestants are given a dataset containing the movie rating histories of ...

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 ...

Biased ART: A neural architecture that shifts attention toward previously disregarded features following an incorrect prediction
Abstract 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 ...

Self-supervised ARTMAP
Abstract Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semi-supervised learning). In each case input ...

A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG
Abstract Electroencephalogram (EEG) is able to indicate states of mental activity ranging from concentrated cognitive efforts to sleepiness. Such mental activity can be reflected by EEG energy. In particular, intrusion of EEG theta ...

An intelligent ballistocardiographic chair using a novel SF-ART neural network and biorthogonal wavelets
Abstract This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal ...

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 ...

A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm
Abstract This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid ...

Ovarian cancer diagnosis with complementary learning fuzzy neural network
Abstract Early detection is paramount to reduce the high death rate of ovarian cancer. Unfortunately, current detection toot is not sensitive. New techniques such as deoxyribonucleic acid (DNA) micro-array and proteomics data are ...

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 ...

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 ...

DETECTION OF TOOL FAILURE IN END MILLING WITH WAVELET TRANSFORMATIONS AND NEURAL NETWORKS (WT-NN)
Abstract Detection of tool failure is very important in automated manufacturing. In this study, tool failure detection was conducted in two steps by using Wavelet Transformations and Neural Networks (WT-NN). In the first step, data ...

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 ...

On the match tracking anomaly of the ARTMAP neural network
Abstract This article analyses the match tracking anomaly (MTA) of the ARTMAP neural network. The anomaly arises when an input pattern exactly matches its category prototype that the network has previously learned, and the network ...

Adaptive resonance theory based neural network for supervised chemical pattern recognition (FuzzyARTMAP) [1] Theory and network properties
Abstract The FuzzyARTMAP algorithm is studied with respect to its usefulness for supervised chemical pattern recognition. The theory of this relatively complex artificial neural classifier is presented in detail for chemists. An ...

Adaptive resonance theory based neural network for supervised chemical pattern recognition (FuzzyARTMAP) [2] Classification of post-consumer plastics by remote NIR spectroscopy using an InGaAs diode a
Abstract The supervised working FuzzyARTMAP pattern recognition algorithm has been applied to automated identification of post-consumer plastics by near-infrared spectroscopy (NIRS). Experimentally, a remote operating parallel ...

ART-based multiple neural networks for monitoring offshore platforms
Abstract A novel scheme using artificial neural networks to automate the vibration monitoring method of detecting the occurrence and location of damage in offshore jacket platforms is presented. A multiple neural network system is ...

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 ...

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 ...

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 ...

Application of artificial neural networks for the development of a signal monitoring system
Abstract A prototype of a Signal Monitoring System (SMS) utilizing artificial neural networks is developed in this work. The prototype system is unique in: 1) its utilization of state-of-the-art technology in pattern recognition such ...

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 ...

A constructive, incremental-learning network for mixture modeling and classification
Abstract Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of ...

Enhanced exchange heuristic based resource constrained scheduler using ARTMAP
Abstract The Exchange Heuristic (EH) has demonstrated superior results compared with other RCS methods in solving Resource Constrained Scheduling (RCS) problems. Selecting the most promising target constitutes the success of EH. The ...

Comparative analysis of fuzzy ART and ART-2A network clustering performance
Abstract Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. Many different types of ART-networks have been ...

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 ...

Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2A
Abstract Aerosol particles have received significant public and scientific attention in recent years due to studies linking them to global climatic changes and human health effects. In 1994, Prather et al, (Prather, K. A.; Nordmeyer, ...

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 ...

Generalization, discrimination, and multiple categorization using adaptive resonance theory
Abstract The internal competition between categories in the adaptive resonance theory (ART) neural model can be biased by replacing the original choice function by one that contains an attentional tuning parameter under external ...

Fuzzy ARTMAP based electronic nose data analysis
Abstract The Fuzzy ARTMAP neural network is a supervised pattern recognition method based on fuzzy adaptive resonance theory (ART). It is a promising method since Fuzzy ARTMAP is able to carry out on-line learning without forgetting ...

Self-organizing arterial pressure pulse classification using neural networks: theoretical considerations and clinical applicability
Abstract A self-organizing classification system for the arterial pressure pulse based on the ART2 (adaptive resonance theory) network was developed. The system consists of a preprocessor and an ART2 recognition network. The ...

Determining temporal pattern of community dynamics by using unsupervised learning algorithms
Abstract Analysis of patterns of temporal variation in community dynamics was conducted by combining two unsupervised artificial neural networks, the Adaptive Resonance Theory (ART) and the Kohonen network. The field data used as ...

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 ...

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 ...

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 ...

Unsupervised adaptive resonance theory neural networks for control chart pattern recognition
Abstract This paper describes the use of unsupervised adaptive resonance theory ART2 neural networks for recognizing patterns in statistical process control charts. To improve the classification accuracy, three schemes are proposed. ...

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 ...

Mining a Web citation database for document clustering
Abstract The World Wide Web has become an important medium for disseminating scientific publications. Many publications are now made available over the Web. However, existing search engines are ineffective in searching these ...

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 ...

A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network
Abstract With the increasing interest and emphasis on customer demands in new product development, customer requirements elicitation (CRE) and evaluation have secured a crucial position in the early stage of product ...

Online pattern classification with multiple neural network systems: An experimental study
Abstract In this paper, an empirical study of the development and application of a committee of neural networks on online pattern classification tasks is presented. A multiple classifier framework is designed by adopting an Adaptive ...

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 ...

Screening of stress enhancer based on analysis of gene expression profiles: Enhancement of hyperthermia-induced tumor necrosis by an MMP-3 inhibitor
Abstract To improve the therapeutic benefit of hyperthermia, we examined changes of global gene expression after heat shock using DNA microarrays consisting of 12 814 clones. HeLa cells were treated for 1 h at 44degreesC and RNA was ...

Inference of common genetic network using fuzzy adaptive resonance theory associated matrix methods
Abstract Inferring genetic networks from gene expression data is the most challenging work in the postgenomic era. However, most studies tend to show their genetic network inference ability by using artificial data. Here, we ...

On-line tool breakage monitoring in turning
Abstract Acoustic emission (AE) and motor power sensors were used to detect the tool breakage in turning. Time-frequency analysis was used to process different AE signals emitted from the cutting process (normal cutting condition, ...

Performance of Fuzzy ART neural network and hierarchical clustering for part-machine grouping based on operation sequences
Abstract The problem context for this study is one of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time ...

Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems
Abstract An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out. from both qualitative and quantitative points of view. The study involves ...

A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations
Abstract In this paper, we present a methodology for automatic diagnosis of systems characterized by continuous signals. For each condition considered, the methodology requires the development of an alphabet of signal primitives, and ...

Polytope ARTMAP: Pattern classification without vigilance based on general geometry categories
Abstract This paper proposes polytope ARTMAP (PTAM), an adaptive resonance theory (ART) network for classification tasks which does not use the vigilance parameter.. This feature is due to the geometry of categories in PTAM, which ...

Manufacturing quality control by means of a Fuzzy ART network trained on natural process data
Abstract In order to produce products with constant quality, manufacturing systems need to be monitored for any unnatural deviations in the state of the process. Control charts have an important role in solving quality control ...

A proteomic analysis of maize chloroplast biogenesis
Abstract Proteomics studies to explore global patterns of protein expression in plant and green algal systems have proliferated within the past few years. Although most of these studies have involved mapping of the proteomes of ...

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 ...

Modified ART 2A growing network capable of generating a fixed number of nodes
Abstract This paper introduces the Adaptive Resonance Theory under Constraint (ART-C 2A) learning paradigm based on ART 2A, which is capable of generating a user-defined number of recognition nodes through online estimation of an ...

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. ...

Trichromatic sorting of in vitro regenerated plants of gladiolus using adaptive resonance theory
Abstract A machine vision system is described to sort the regenerated plants of gladiolus into groups using trichromatic features of leaves. The machine vision system consisted of a scanner, image analysis software and an adaptive ...

A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function
Abstract In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized ...

Clustering in wavelet domain: A multiresolution ART network for anomaly detection
Abstract A method for process fault detection is presented, based on the integration of multiscale signal representation and scale-specific clustering-based diagnosis. Previous work has demonstrated the utility of our multiscale ...

Multi-class cancer classification by semi-supervised ellipsoid ARTMAP with gene expression data
Abstract To accurately identify the site of origin of a tumor is crucial to cancer diagnosis and treatment. With the emergence of DNA microarray technologies, constructing gene expression profiles for different cancer types has ...

Adaptive Resonance Theory-based neural algorithms for manufacturing process quality control
Abstract The demand for quality products in industry is continuously increasing. To produce products with consistent quality, manufacturing systems need to be closely monitored for any unnatural deviation in the state of the process. ...

A hybrid neural network model for noisy data regression
Abstract A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning ...

Time-course data analysis of gene expression profiles reveals purR regulon concerns in organic solvent tolerance in Escherichia coli
Abstract A time-course gene-expression profile was generated for Escherichia coli TK31 when it was exposed to an organic solvent mixture, and classified by fuzzy adaptive resonance theory (Fuzzy ART). It was found that the purR ...

A new ART-counterpropagation neural network for solving a forecasting problem
Abstract This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the ...

Fuzzy-ART based adaptive digital watermarking scheme
Abstract In this paper, a novel transform domain digital watermarking scheme that uses visually meaningful binary image as watermark has been developed. The method embeds the watermark information adaptively with localized embedding ...

Fuzzy ART neural network algorithm for classifying the power system faults
Abstract This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive ...

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, ...

Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce
Abstract Neural networks and genetic algorithms are useful for clustering analysis in data mining. Artificial neural networks (ANNs) and genetic algorithms (GAs) have been applied in many areas with very promising results. Thus, this ...

Predicting bulk ambient aerosol compositions from ATOFMS data with ART-2a and multivariate analysis
Abstract The aerosol time-of-flight mass spectrometry (ATOFMS) has not generally been used to provide a quantitative estimation of chemical compositions of ambient aerosols. In an initial study, the possibility of developing a ...

Understanding ART-based neural algorithms as statistical tools for manufacturing process quality control
Abstract Neural networks have recently received a great deal of attention in the field of manufacturing process quality control, where statistical techniques have traditionally been used. In this paper, a neural-based procedure for ...

ART artificial neural networks based adaptive phase selector
Abstract This paper introduces a new phase selector based on adaptive resonance theory (ART). Because conventional phase selector cannot adapt dynamically to the power system operating conditions, it presents different characters ...

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 ...

Hybrid feature vector extraction in unsupervised learning neural classifier
Abstract Feature extraction and selection method as a preliminary stage of heart rate variability (HRV) signals unsupervised learning neural classifier is presented. Multi-domain, mixed new feature vector is created from time, ...

Automated categorization of bioacoustic signals: Avoiding perceptual pitfalls
Abstract Dividing the acoustic repertoires of animals into biologically relevant categories presents a widespread problem in the study of animal sound communication, essential to any comparison of repertoires between contexts, ...

Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor
Abstract Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was ...


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 ...

Self-Supervised ARTMAP
Description Self-Supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled ...

SMART network
Description This entry contains the software, implemented in the KDE Integrated NeuroSimulation Software (KInNeSS ) that simulates the Synchronous Matching Adaptive Resonance Theory. SMART was first described in Grossberg and Versace ...


Datasets


Boston Remote Sensing Testbed (preprocessed MAT files)
Description

These MATLAB-ready .mat data files contain pixel data for the Boston remote sensing testbed. This is the same data that is provided with Classer. However, all the Classer preprocessing scripts have been run, so the mat file

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