D.C. Wunsch II

GE Global Research, Niskayuna, New York, United States

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Publications (67)26.25 Total impact

  • Rui Xu, Donald C Wunsch Ii
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    ABSTRACT: Clustering has been used extensively in the analysis of high-throughput messenger RNA (mRNA) expression profiling with microarrays. Furthermore, clustering has proven elemental in microRNA expression profiling, which demonstrates enormous promise in the areas of cancer diagnosis and treatment, gene function identification, therapy development and drug testing, and genetic regulatory network inference. However, such a practice is inherently limited due to the existence of many uncorrelated genes with respect to sample or condition clustering, or many unrelated samples or conditions with respect to gene clustering. Biclustering offers a solution to such problems by performing simultaneous clustering on both dimensions, or automatically integrating feature selection to clustering without any prior information, so that the relations of clusters of genes (generally, features) and clusters of samples or conditions (data objects) are established. However, the NP-complete computational complexity raises a great challenge to computational methods for identifying such local relations. Here, we propose and demonstrate that a neural-based classifier, ARTMAP, can be modified to perform biclustering in an efficient way, leading to a biclustering algorithm called Biclustering ARTMAP (BARTMAP). Experimental results on multiple human cancer data sets show that BARTMAP can achieve clustering structures with higher qualities than those achieved with other commonly used biclustering or clustering algorithms, and with fast run times.
    Neural networks: the official journal of the International Neural Network Society 04/2011; 24(7):709-16. · 1.88 Impact Factor
  • Robert Woodley, Warren Noll, Joseph Barker, Donald C. Wunsch II
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    ABSTRACT: Given the vast amount of image intelligence utilized in support of planning and executing military operations, a passive automated image processing capability for target identification is urgently required. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive Resonance Theory approach to cluster templates of target buildings. The results show that the network successfully classifies targets from non-targets in a virtual test bed environment.
    Proc SPIE 05/2009;
  • Rui Xu, Donald C. Wunsch II
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    ABSTRACT: Purpose – The purpose of this paper is to provide a review of the issues related to cluster analysis, one of the most important and primitive activities of human beings, and of the advances made in recent years. Design/methodology/approach – The paper investigates the clustering algorithms rooted in machine learning, computer science, statistics, and computational intelligence. Findings – The paper reviews the basic issues of cluster analysis and discusses the recent advances of clustering algorithms in scalability, robustness, visualization, irregular cluster shape detection, and so on. Originality/value – The paper presents a comprehensive and systematic survey of cluster analysis and emphasizes its recent efforts in order to meet the challenges caused by the glut of complicated data from a wide variety of communities.
    International Journal of Intelligent Computing and Cybernetics 10/2008; 1(4):484-508.
  • Rui Xu, Robert S. Woodley, Donald C. Wunsch II
    Proceedings of The 2008 International Conference on Data Mining, DMIN 2008, July 14-17, 2008, Las Vegas, USA, 2 Volumes; 01/2008
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    Rui Xu, Donald Wunsch Ii, Ronald Frank
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    ABSTRACT: Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics 11/2007; 4(4):681-92. · 1.62 Impact Factor
  • Rui Xu, Georgios C. Anagnostopoulos, Donald C. Wunsch II
    05/2007: pages 1 - 20; , ISBN: 9780470199091
  • Nian Zhang, Donald C. Wunsch II
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    ABSTRACT: An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algo-rithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose a neural network based dual heuristic programming (DHP) algorithm to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results were compared with those of stochastic optimization algorithm. It showed that the performance of the dual heuristic programming (DHP) is better than the stochastic optimization method.
    Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II; 01/2006
  • Rui Xu, Ganesh K. Venayagamoorthy, Donald C. Wunsch II
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    ABSTRACT: Gene regulatory inference from time series gene expression data, generated from DNA microarray, has become increasingly important in investigating genes functions and unveiling fundamental cellular processes. Computational methods in machine learning and neural networks play an active role in analyzing the obtained data. Here, we investigate the performance of particle swarm optimization (PSO) on the reconstruction of gene networks, which is modeled with recurrent neural networks (RNN). The experimental results on a synthetic data set are presented to show the parameter effects of PSO on RNN training and the effectiveness of the proposed method in revealing the gene relations.
    Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part III; 01/2006
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    ABSTRACT: Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller overall sizes and therefore of lower cost. This equipment can be used for evaluation of different AI methods in an easy and inexpensive way. The methods that show good results can be transferred to the industry through appropriate scaling. This paper contains brief description of low cost microequipment prototypes and some AI methods that can be evaluated with mentioned prototypes. Full Text at Springer, may require registration or fee
    Toward Category-Level Object Recognition; 01/2006
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    Wenxin Liu, G.K. Venayagamoorthy, D.C. Wunsch II
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    ABSTRACT: Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared with the CPSS and the indirect-adaptive-neurocontrol-based PSS under small and large disturbances. In addition, the impact of different discount factors in the HDP PSS's performance is presented.
    IEEE Transactions on Industry Applications 10/2005; · 1.67 Impact Factor
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    ABSTRACT: A reactive fuzzy logic based control strategy was developed for mobile robot navigation. To decrease the number of fuzzy rules and related processing, a RAM-based neural network was combined with the fuzzy logic strategy. The fuzzy rules are used to interpret sensor information. The neural network uses results from the fuzzy logic as well as environmental information to make navigation decisions. The feasibility of this neuro-fuzzy approach was demonstrated on a mobile robot using a simple, 8-bit microcontroller. Experiments show the approach works well, as the robot was able to successfully avoid objects while seeking a goal in real-time. The neuro-fuzzy approach is code-efficient, fast, and easy to relate to the physical world
    Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on; 06/2005
  • Nian Zhang, D.C. Wunsch II
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    ABSTRACT: To overcome the shortcomings of fully analog and fully digital implementation of artificial neural networks (ANNs), we adopted mixed analog/digital technique. We proposed a switched-resistor (SR) element as a programmable synapse. The switched-resistor implementation of synapse captures both the advantages of analog implementation and the programmability of digital implementation. We also designed a CMOS analog neuron that performs a near-tanh nonlinearity function. We evaluated the performance of the neural networks using Pspice. The results showed that our approach can successfully implement the neural network, and exhibit a very high modularity
    Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on; 06/2005
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    Rui Xu, D. Wunsch II
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    ABSTRACT: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
    IEEE Transactions on Neural Networks 06/2005; · 2.95 Impact Factor
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    Wenxin Liu, S. Jagannathan, D.C. Wunsch II, M.L. Crow
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    ABSTRACT: A novel decentralized neural network (DNN) controller is proposed for a class of large-scale nonlinear systems with unknown interconnections. The objective is to design a DNN for a class of large-scale systems which do not satisfy the matching condition requirement. The NNs are used to approximate the unknown subsystem dynamics and the interconnections. The DNN is designed using the back stepping methodology with only local signals for feedback. All of the signals in the closed loop (system states and weights estimation errors) are guaranteed to be uniformly ultimately bounded and eventually converge to a compact set.
    Decision and Control, 2004. CDC. 43rd IEEE Conference on; 01/2005
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    Rui Xu, D.C. Wunsch II
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    ABSTRACT: Large-scale time series gene expression data generated from DNA microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand their relations and interactions. To infer gene regulatory networks from these data with effective computational tools has attracted intensive efforts from artificial intelligence and machine learning. Here, we use a recurrent neural network (RNN), trained with particle swarm optimization (PSO), to investigate the behaviors of regulatory networks. The experimental results, on a synthetic data set and a real data set, show that the proposed model and algorithm can effectively capture the dynamics of the gene expression time series and are capable of revealing regulatory interactions between genes.
    Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on; 01/2005
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    E. Kussul, T. Baidyk, D.C. Wunsch II
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    ABSTRACT: A feature extractor and neural classifier for image recognition system are proposed. They are based on the permutative coding technique which continues our investigations on neural networks. It permits us to obtain sufficiently general description of the image to be recognized. Different types of images were used to test the proposed image recognition system. It was tested on the handwritten digit recognition problem, the face recognition problem and the shape of microobjects recognition problem. The results of testing are very promising. The error rate for the MNIST database is 0.44% and for the ORL database is 0.1%.
    Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on; 01/2005
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    ABSTRACT: To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the recurrent neural network for the time series prediction. The experimental results show that our approach gives good performance in predicting the missing values from the time series.
    Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on; 08/2004
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    X. Hu, D.C. Wunsch II
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    ABSTRACT: This paper describes the use of a multi-stream extended Kalman filter (EKF) to tackle the IJCNN 2004 challenge problem - time series prediction on CATS benchmark. A weighted bidirectional approach was adopted in the experiments to incorporate the forward and backward predictions of the time series. EKF is a practical, general approach to neural networks training. It consists of the following: 1) gradient calculation by backpropagation through time (BPTT); 2) weight updates based on the extended Kalman filter; and 3) data presentation using multi-stream mechanics.
    Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on; 08/2004
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    ABSTRACT: Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. This paper proposes a stable neural network (NN) controller for the stabilization of a single machine infinite bus power system. In the power system control literature, simplified-analytical models are used to represent the power system and the controller designs are not based on rigorous stability analysis. This work overcomes the two major problems by using an accurate analytical model for controller development and presents the closed-loop stability analysis. The NN is used to approximate the complex nonlinear power system online and the weights of which can be set to zero to avoid the time consuming offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the Lyapunov stability analysis. Simulation results demonstrate that the proposed design can successfully damp out oscillations. The control algorithms of This work can also be applied to other similar control problems.
    Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on; 08/2004
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    R. Dua, D.G. Beetner, W.V. Stoecker, D.C. Wunsch II
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    ABSTRACT: Variations in electrical impedance over frequency might be used to distinguish basal cell carcinoma (BCC) from benign skin lesions, although the patterns that separate the two are nonobvious. Artificial neural networks (ANNs) may be good pattern classifiers for this application. A preliminary study to show the potential of neural networks to distinguish benign from malignant skin lesions using electrical impedance is presented. Electrical impedance was measured in vivo from 1 kHz to 1 MHz at five virtual depths on 18 BCC and 16 benign or premalignant lesions. A feed-forward neural network was trained using back propagation to classify these lesions. Two methods of preprocessing were used to account for the impedance of normal skin and the size of the lesion, one based on estimating the impedance of the lesion relative to adjacent normal skin and one based on estimating the impedance of the lesion independent of size or surrounding normal skin. Neural networks were able to classify measurements in a test set with 100% accuracy for the first preprocessing technique and 85% accuracy for the second. These results indicate electrical impedance may be a promising clinical diagnostic tool for basal cell carcinoma or other forms of skin cancer.
    IEEE Transactions on Biomedical Engineering 02/2004; · 2.35 Impact Factor

Publication Stats

2k Citations
26.25 Total Impact Points

Institutions

  • 2011
    • GE Global Research
      Niskayuna, New York, United States
  • 2008–2011
    • Missouri University of Science and Technology
      • Department of Electrical Engineering
      Missouri, United States
  • 2007
    • Heriot-Watt University
      Edinburgh, Scotland, United Kingdom
  • 2001–2006
    • University of Missouri
      • Department of Electrical and Computer Engineering
      Columbia, Missouri, United States
  • 2005
    • South Dakota School of Mines and Technology
      • Department of Electrical and Computer Engineering
      Rapid City, SD, United States
    • Universidad Nacional Autónoma de México
      Ciudad de México, The Federal District, Mexico
    • Florida State University
      • Center for Advanced Power Systems (CAPS)
      Tallahassee, FL, United States
  • 2003
    • Southern Methodist University
      Dallas, Texas, United States
  • 1993–1999
    • Texas Tech University
      • Department of Electrical and Computer Engineering
      Lubbock, Texas, United States
  • 1998
    • Krasnoyarsk Scientific Center
      Красноярск, Krasnoyarskiy, Russia
  • 1997
    • Krasnoyarsk State University
      Krasnoyarskiy, Rostov, Russia