D.C. Wunsch II

Missouri University of Science and Technology, Missouri, United States

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Publications (76)

  • George J. Shannon · Steven M. Corns · Donald Wunsch II
    [Show abstract] [Hide abstract] ABSTRACT: This research demonstrates the use of genetic programming to derive the objective function that ranks the candidate concepts and selects the set of best matching concepts for a sentence within medical text. A short set of example primitive and linguistic variables was input into the GP process, and a set of manually tagged sentences extracted from the literature was used to derive different objective functions potentially suitable for tagging. This proof-of-concept demonstrates the potential of this approach to simplify automated semantic tagging and to identify some of the likely challenges of applying the GP approach to complex linguistics problems of this nature.
    Conference Paper · May 2014
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    Ashraf M. Abdelbar · Donald C. Wunsch II
    [Show abstract] [Hide abstract] ABSTRACT: In ant colony optimization (ACO) methods, including Ant System and MAX-M IN Ant System, each ant stochastically generates its candidate solution, in a given iteration, based on the same pheromone T and heuristic η information as every other ant. Stubborn ants is an ACO variation in which if an ant generates a particular candidate solution in a given iteration, then the components of that solution will have a higher probability of being selected in the candidate solution generated by that ant in the next iteration. In previous work, we evaluated this variation with the M M AS Ant System model and the Traveling Salesman Problem (TSP), and found that it can both improve solution quality and reduce execution-time. In this paper, we evaluate stubborn ants with Ranked Ant System, and find that performance also improves in terms of solution quality and execution time.
    Full-text Article · Dec 2012 · Procedia Computer Science
  • Rui Xu · Donald C Wunsch Ii
    [Show abstract] [Hide abstract] 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.
    Article · Apr 2011 · Neural networks: the official journal of the International Neural Network Society
  • Prof. Ernst Kussul · Prof. Tatiana Baidyk · Prof. Donald C. Wunsch II
    [Show abstract] [Hide abstract] ABSTRACT: A feature extractor and neural classifier for a face image recognition system are proposed. They are based on the Permutation Coding technique, which continues our investigation of neural networks. The permutation coding technique makes it possible to take into account not only detected features, but also the position of each feature in the image. It permits us to obtain a 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 and the face recognition problem. The results of this test are very promising. The error rate for the MNIST database is 0.44%, and for the ORL database it is 0.1%. In the last section, which is devoted to micromechanics applications, we will describe the application of the permutation coding technique to the micro-object shape recognition problem.
    Chapter · Jan 2010
  • Robert Woodley · Warren Noll · Joseph Barker · Donald C. Wunsch II
    [Show abstract] [Hide abstract] 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.
    Article · May 2009 · Proceedings of SPIE - The International Society for Optical Engineering
  • Rui Xu · Donald C. Wunsch II
    [Show abstract] [Hide abstract] 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.
    Article · Oct 2008 · International Journal of Intelligent Computing and Cybernetics
  • Rui Xu · Robert S. Woodley · Donald C. Wunsch II
    [Show abstract] [Hide abstract] ABSTRACT: The possibility of the usage of deadly aerosolized pathogens, particularly anthrax, in bioterrorist attack has raised tremendous concerns in recent years. Several anthrax incubation models have been introduced in order to characterize the incubation period of human inhalation anthrax. It is important to accurately identify the model that fits best with the observed anthrax time series, which directly affects the prediction results of the severity of the potential anthrax attacks. Here, we applied Default ARTMAP, an important neural network algorithm for classification, to separate anthrax time series generated from different inhalation anthrax models. Experimental results on anthrax time series derived from major inhalation anthrax models, together with anti-patterns and a smallpox time series, demonstrate the effectiveness of Default ARTMAP in identifying anthrax time series derived from different models, as well as discriminating unrelated cases.
    Conference Paper · Jan 2008
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    Rui Xu · Donald Wunsch Ii · Ronald L. Frank
    [Show abstract] [Hide abstract] 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.
    Full-text Article · Nov 2007 · IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • Xiao Hu · Danil V. Prokhorov · Donald C. Wunsch II
    [Show abstract] [Hide abstract] ABSTRACT: We use a multi-stream extended Kalman filter for the CATS benchmark (Competition on Artificial Time Series), to train recurrent multilayer perceptrons. A weighted bidirectional approach is adopted to combine forward and backward predictions and to generate the final predictions on the missing points.
    Article · Aug 2007
  • Rui Xu · Georgios C. Anagnostopoulos · Donald C. Wunsch II
    Chapter · May 2007
  • Nian Zhang · Donald C. Wunsch II
    [Show abstract] [Hide abstract] 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.
    Conference Paper · May 2006
  • Rui Xu · Ganesh K. Venayagamoorthy · Donald C. Wunsch II
    [Show abstract] [Hide abstract] 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.
    Conference Paper · May 2006
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    Ernst M. Kussul · Tatiana Baidyk · Felipe Lara-Rosano · [...] · Donald C. Wunsch II
    [Show abstract] [Hide abstract] 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
    Full-text Conference Paper · Jan 2006
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    Wenxin Liu · G.K. Venayagamoorthy · D.C. Wunsch II
    [Show abstract] [Hide abstract] 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.
    Full-text Article · Oct 2005 · IEEE Transactions on Industry Applications
  • Nian Zhang · D. Beetner · D.C. Wunsch II · [...] · A. Hasan
    [Show abstract] [Hide abstract] 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
    Conference Paper · Jun 2005
  • Nian Zhang · D.C. Wunsch II
    [Show abstract] [Hide abstract] 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
    Conference Paper · Jun 2005
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    Rui Xu · D. C. Wunsch II
    [Show abstract] [Hide abstract] 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.
    Full-text Article · Jun 2005 · IEEE Transactions on Neural Networks
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    Wenxin Liu · S. Jagannathan · D.C. Wunsch II · M.L. Crow
    [Show abstract] [Hide abstract] 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.
    Full-text Conference Paper · Jan 2005
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    Rui Xu · D.C. Wunsch II
    [Show abstract] [Hide abstract] 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.
    Full-text Conference Paper · Jan 2005
  • E. Kussul · T. Baidyk · D.C. Wunsch II
    [Show abstract] [Hide abstract] 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%.
    Conference Paper · Jan 2005

Publication Stats

3k Citations


  • 2008-2012
    • Missouri University of Science and Technology
      • Department of Electrical Engineering
      Missouri, United States
  • 2001-2006
    • University of Missouri
      • Department of Electrical and Computer Engineering
      Columbia, Missouri, United States
  • 1995-2000
    • 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