Fuzzy Adaptive Resonance Theory Mapping Research Articles

Imbalanced dataset classification using fuzzy ARTMAP and computational intelligence techniques

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

PurposeIn time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.Design/methodology/approachIn this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.FindingsThe proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.Research limitations/implicationsResearch in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.Originality/valueThe proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

An Enhanced Weight Update Method for Simplified ARTMAP to Classify Groundwater Data

The groundwater for aquatic purposes must be assessed prior to its consumption. Huge number of conventional methods are existing for assessing the quality of groundwater. The water quality index is one of the important conventional methods to assess the groundwater quality. But the conventional methods alone are not enough to assess groundwater quality as well as classify based on its purity. In this paper, we propose an enhanced weight update method for Simplified Fuzzy Adaptive Resonance Theory model to classify the groundwater quality depending on the relative weights of the groundwater quality parameters. Finding the optimal weights is the key to achieve better accuracy of the model, most of the nonlinear models fails to exhibit good accuracy if they fail to learn the optimal weights in the learning process. The aim of the work is to find the good fit between the predicted and the actual groundwater quality grades by identifying the optimal weights of the network by the enhanced weight update method. The Simplified Fuzzy Adaptive Resonance Theory map with the enhanced weight update method performance is justified by comparing it with the Simplified Fuzzy Adaptive Resonance Theory Map. The enhanced weight update method improves the accuracy of the Simplified Fuzzy Adaptive Resonance Theory Map in classifying and predicting the groundwater quality.

HearthBot: An Autonomous Agent Based on Fuzzy ART Adaptive Neural Networks for the Digital Collectible Card Game HearthStone

Digital collectible card games, as partially observable games based on alternating turns, such as HearthStone, have been the most played card games in recent years, where the main challenge is the creation of strategies capable of subdue the enemy's moves. From the artificial intelligence perspective, the space of possible strategies is large and dynamic due to the number of cards and actions combinations and also to randomness, which makes the design of efficient autonomous agents a hard problem. This paper presents HearthBot, an autonomous agent that plays HearthStone through an adaptive neural network inspired in the fuzzy adaptive resonance associative map and adaptive resonance theory map. This paper also proposes a new mechanism to categorize and predict information to overcome the overgeneralization problem from those networks. Furthermore, the proposed solution was implemented as a parallel adaptive neural network for a graphics processing unit that achieves a performance compatible with the ones obtained for deep learning methods. HearthBot win rate was evaluated in two experiments playing against a Monte Carlo tree search heuristic with competitive decks on a HearthStone simulator called Metastone. Results show that the proposed solution allows HearthBot to obtain an average win rate performance of 80% against known decks and 70% against unknown decks.

Online residual useful life prediction of large-size slewing bearings—A data fusion method

To decrease breakdown time and improve machine operation reliability, accurate residual useful life (RUL) prediction has been playing a critical role in condition based monitoring. A data fusion method was proposed to achieve online RUL prediction of slewing bearings, which consisted of a reliability based RUL prediction model and a data driven failure rate (FR) estimation model. Firstly, an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR. Secondly, principal component analysis (PCA) was introduced to process multi-dimensional life-cycle vibration signals, and continuous squared prediction error (CSPE) and its time-domain features were employed as equipment performance degradation features. Afterwards, an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map (SFAM) neural network. Consequently, real-time FR of equipment can be obtained through FR estimation model, and then accurate RUL can be calculated through the RUL prediction model. Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings, and that by combining actual load condition and real-time monitored data, the calculation time is reduced by 87.3% and the accuracy is increased by 0.11%, which provides a potential for online RUL prediction of slewing bearings and other various machineries.

A Novel Clustering Method Combining ART with Yu’s Norm for Fault Diagnosis of Bearings

Clustering methods have been widely applied to the fault diagnosis of mechanical system, but the characteristic that the number of cluster needs to be determined in advance limits the application range of the method. In this paper, a novel clustering method combining the adaptive resonance theory (ART) with the similarity measure based on the Yu’s norm is presented and applied to the fault diagnosis of rolling element bearings, which can be adaptive to generate the number of cluster by the vigilance parameter test. Time-domain features, frequency-domain features, and time series model parameters are extracted to demonstrate the fault-related information about the bearings, and then considering the irrelevance or redundancy of some features many salient features are selected by an improved distance discriminant technique and input into the proposed clustering method to diagnose the faults of bearings. The experiment results confirmed that the proposed clustering method can diagnose the fault categories accurately and has better diagnosis performance compared with fuzzy ART and Self-Organizing Feature Map (SOFM).

Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations

In this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD). Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN–SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD–LDA–PNN–SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it is validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets.

Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine’s breakdown and maintenance’s cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques.In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.

Affect classification using genetic-optimized ensembles of fuzzy ARTMAPs

Training neural networks in distinguishing different emotions from physiological signals frequently involves fuzzy definitions of each affective state. In addition, manual design of classification tasks often uses sub-optimum classifier parameter settings, leading to average classification performance. In this study, an attempt to create a framework for multi-layered optimization of an ensemble of classifiers to maximize the system's ability to learn and classify affect, and to minimize human involvement in setting optimum parameters for the classification system is proposed. Using fuzzy adaptive resonance theory mapping (ARTMAP) as the classifier template, genetic algorithms (GAs) were employed to perform exhaustive search for the best combination of parameter settings for individual classifier performance. Speciation was implemented using subset selection of classification data attributes, as well as using an island model genetic algorithms method. Subsequently, the generated population of optimum classifier configurations was used as candidates to form an ensemble of classifiers. Another set of GAs were used to search for the combination of classifiers that would result in the best classification ensemble accuracy. The proposed methodology was tested using two affective data sets and was able to produce relatively small ensembles of fuzzy ARTMAPs with excellent affect recognition accuracy.

Identification of early-stage Alzheimer׳s disease using SFAM neural network

Alzheimer׳s disease (AD), the most common form of dementia, is a complex and very serious nervous disease. Currently no medication is really effective against it. Even, its diagnosis and management remain challenging research problem for scientists. This paper aims towards identifying early-stage AD based upon the characteristics of the non-oscillatory independent components (ICs) of the auditory event related potential (AERP) waveforms of an oddball task for healthy and newly diagnosed AD subjects. Using 27 sensors to record P300 Evoked Potentials (EPs) as features vectors to train the simplified fuzzy adaptive resonance theory map (SFAM) neural network as a classifier, normal and AD subjects were classified with higher than 95% of success. The use of the ARTMAP-familiarity discrimination (ARTMAP-FD) shows that the separation of the two populations was achieved with high success and without any mistake.

Hybrid Method for Vehicle Detection from CCTV Captured Image

This paper presents a hybrid method for vehicle detection from CCTV captured image. In order to overwhelm such complex details of the color image, the system combines artificial intelligence techniques to achieve automatic vehicle detection. These are techniques 2D principal component analysis (2DPCA), Fuzzy adaptive resonance theory (Fuzzy ART), genetic algorithm (GA) and self-organizing map. The proposed system can detect different vehicle sizes from different proportional image area. Bilinear interpolation is used to resize each proportional image area to vehicle feature matrix. The proposed system can detect various types of vehicles from the difference image background.

Development of an Integrated Adaptive Resonance Theory Mapping Classification System for Supporting Watershed Hydrological Modeling

AbstractAs it is a critical process of watershed management, classification always faces challenges of inefficiency in handling complexity and uncertainty. This study attempts to fill this gap by developing an integrated adaptive resonance theory mapping system consisting of a two-stage adaptive resonance theory mapping (TSAM) approach and an integrated rule-based fuzzy adaptive resonance theory mapping (IRFAM) approach. To demonstrate their feasibility and efficiency, TSAM and IRFAM were compared with conventional adaptive resonance theory mapping (ARTMap) in two case studies in the Deer River watershed in Manitoba, Canada, which were classifications of watershed subbasins and types of land-cover to support hydrological modeling. Among the three approaches, IRFAM performed best in effectively processing the classification for input patterns with a high level of uncertainty and a wide range of variations, although it required predefined criteria. TSAM performed reasonably well by generating criteria for s...

Motor imaginary-based brain–machine interface design using programmable logic controllers for the disabled

Aiming at the implementation of brain–machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain's motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3 A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.

IRFAM: Integrated Rule-Based Fuzzy Adaptive Resonance Theory Mapping System for Watershed Modeling

Watersheds are featured by a variety of hydrological, meteorological, and ecological characteristics. Complexity and uncertainty are usually two major challenges during watershed classification which is one of the key processes in hydrological modeling. This study aims to develop an integrated rule-based fuzzy adaptive resonance theory mapping (IRFAM) system, by incorporating fuzzification and rule-based operation to more efficiently handle the complexity and uncertainty. The developed system has been tested with a case study conducted in the Deer River watershed in Manitoba, Canada. The results are further compared with the ones generated by the conventional adaptive resonance theory mapping (ARTMap) method. All subbasins are classified by IRFAM while ARTMap leaves five subbasins unclassified. Furthermore, another nine subbasins in the juncture between classified groups from the ARTMap classification results are relocated by IRFAM. The IRFAM system can take advantage of fuzzy set theory to generate full ...

Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP

This paper presents three hitherto unused neural network architectures for bankruptcy prediction in banks. These networks are Group Method of Data Handling (GMDH), Counter Propagation Neural Network (CPNN) and fuzzy Adaptive Resonance Theory Map (fuzzy ARTMAP). Efficacy of each of these techniques is tested by using four different datasets pertaining to Spanish banks, Turkish banks, UK banks and US banks. Further t-statistic, f-statistic and GMDH are used for feature selection purpose and the features so selected are fed as input to GMDH, CPNN and fuzzy ARTMAP for classification purpose. In each of these cases, top five features are selected in the case of Spanish dataset and top seven features are selected in the case of Turkish and UK datasets. It is observed that the features selected by t-statistic and f-statistic are identical in all datasets. Further, there is a good overlap in the features selected by t-statistic and GMDH. The performance of these hybrids is compared with that of GMDH, CPNN and fuzzy ARTMAP in their stand-alone mode without feature selection. Ten-fold cross validation is performed throughout the study. Results indicate that the GMDH outperformed all the techniques with or without feature selection. Furthermore, the results are much better than those reported in previous studies on the same datasets in terms of average accuracy, average sensitivity and average specificity.

An Evolving Fuzzy Neural Network Based on the Mapping of Similarities

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Evolving fuzzy systems (EFSs) use online learning to extract knowledge from data, perform a high-level adaptation of the network structure, and learn parameters. In this paper, we describe the performance of an EFS that is called similarity mapping, where the training pairs (<formula formulatype="inline"><tex Notation="TeX">${\bf x}_i$</tex></formula> and <formula formulatype="inline"><tex Notation="TeX">${\bf z}_i$</tex></formula>) are compressed into input and output clusters. The predictive error is minimized using a procedure that is very similar to the one implemented in fuzzy-adaptive resonance theory map (ARTMAP) and resource-allocation networks. However, in the recall phase, a fuzzy membership grade is calculated for each input cluster and used in the weighting of the output clusters to obtain the final output vector. By modifying the spread of the cluster membership function, different approximative interpolating functions can be implemented. A similarity function, which was initially proposed for ART 1 implementations, is extended to the processing of analog vectors and used to calculate the membership grades of the input clusters. Several examples show the behavior of the network, as well as its capability to classify, eliminate noise, and predict chaotic time series. </para>

Total luminescence spectroscopy with pattern recognition for classification of edible oils.

Total luminescence spectroscopy combined with pattern recognition has been used to discriminate between four different types of edible oils, extra virgin olive (EVO), non-virgin olive (NVO), sunflower (SF) and rapeseed (RS) oils. Simplified fuzzy adaptive resonance theory mapping (SFAM), traditional back propagation (BP) and radial basis function (RBF) neural networks provided 100% classification for 120 samples, SFAM was found to be the most efficient. The investigation was extended to the adulteration of percentage v/v SF or RS in EVO at levels from 5% to 90% creating a total of 480 samples. SFAM was found to be more accurate than RBF and BP for classification of adulterant level. All misclassifications for SFAM occurred at the 5% v/v level resulting in a total of 99.375% correctly classified oil samples. The percentage of adulteration may be described by either RBF network (2.435% RMSE) or a simple Euclidean distance relationship of the principal component analysis (PCA) scores (2.977% RMSE) for v/v RS in EVO adulteration.

Investigating Driver Injury Severity in Traffic Accidents Using Fuzzy ARTMAP

This paper applies fuzzy adaptive resonance theory MAP (fuzzy ARTMAP) neural networks to analyze and predict injury severity for drivers involved in traffic accidents. The paper presents a modified version of fuzzy ARTMAP in which the training patterns are ordered using the K–means algorithm before being presented to the neural network. The paper presents three applications of fuzzy ARTMAP for analyzing driver injury severity for drivers involved in accidents on highways, signalized intersections, and toll plazas. The analysis is based on central Florida’s traffic accident database. Results showed that the ordered fuzzy ARTMAP proved to reduce the network size and improved the performance. To facilitate the application of fuzzy ARTMAP, a series of simulation experiments to extract knowledge from the models were suggested. Results of the fuzzy ARTMAP neural network showed that female drivers experience higher severity levels than male drivers. Vehicle speed at the time of an accident increases the likelihood of high injury severity. Wearing a seat belt decreases the chance of having severe injuries. Drivers in passenger cars are more likely to experience a higher injury severity level than those in vans or pickup trucks. Point of impact, area type, driving under the influence, and driver age were also among the factors that influence the severity level.

An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance

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 to as the ordering algorithm, and the combination of this procedure with fuzzy ARTMAP is referred to as ordered fuzzy ARTMAP. Experimental results demonstrate that ordered fuzzy ARTMAP exhibits a generalization performance that is better than the average generalization performance of fuzzy ARTMAP, and in certain cases as good as, or better than the best fuzzy ARTMAP generalization performance. We also calculate the number of operations required by the ordering algorithm and compare it to the number of operations required by the training phase of fuzzy ARTMAP. We show that, under mild assumptions, the number of operations required by the ordering algorithm is a fraction of the number of operations required by fuzzy ARTMAP.

Cascade ARTMAP: integrating neural computation and symbolic knowledge processing

This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neural-network learning and recognition. Cascade ARTMAP, a generalization of fuzzy ARTMAP, represents intermediate attributes and rule cascades of rule-based knowledge explicitly and performs multistep inferencing. A rule insertion algorithm translates if-then symbolic rules into cascade ARTMAP architecture. Besides that initializing networks with prior knowledge can improve predictive accuracy and learning efficiency, the inserted symbolic knowledge can be refined and enhanced by the cascade ARTMAP learning algorithm. By preserving symbolic rule form during learning, the rules extracted from cascade ARTMAP can be compared directly with the originally inserted rules. Simulations on an animal identification problem indicate that a priori symbolic knowledge always improves system performance, especially with a small training set. Benchmark study on a DNA promoter recognition problem shows that with the added advantage of fast learning, cascade ARTMAP rule insertion and refinement algorithms produce performance superior to those of other machine learning systems and an alternative hybrid system known as knowledge-based artificial neural network (KBANN). Also, the rules extracted from cascade ARTMAP are more accurate and much cleaner than the NofM rules extracted from KBANN.