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No-Boundary Thinking In Bioinformatics Research, Xiuzhen Huang, Barry Bruce, Alison Buchan, Clare Bates Congdon, Carole L. Cramer, Steven F. Jennings, Hongmei Jiang, Zenglu Li, Gail McClure, Rick McMullen, Jason H. Moore, Bindu Nanduri, Joan Peckham, Andy Perkins, Shawn W. Polson, Bhanu Rekepalli, Saeed Salem, Jennifer Specker, Donald C. Wunsch, Donghai Xiong, Shuzhong Zhang, Zhongming Zhao 2018 Missouri University of Science and Technology

No-Boundary Thinking In Bioinformatics Research, Xiuzhen Huang, Barry Bruce, Alison Buchan, Clare Bates Congdon, Carole L. Cramer, Steven F. Jennings, Hongmei Jiang, Zenglu Li, Gail Mcclure, Rick Mcmullen, Jason H. Moore, Bindu Nanduri, Joan Peckham, Andy Perkins, Shawn W. Polson, Bhanu Rekepalli, Saeed Salem, Jennifer Specker, Donald C. Wunsch, Donghai Xiong, Shuzhong Zhang, Zhongming Zhao

Donald C. Wunsch

Currently there are definitions from many agencies and research societies defining "bioinformatics" as deriving knowledge from computational analysis of large volumes of biological and biomedical data. Should this be the bioinformatics research focus? We will discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT).


Neurocontroller Alternatives For "Fuzzy" Ball-And-Beam Systems With Nonuniform Nonlinear Friction, Danil V. Prokhorov, Donald C. Wunsch, Paul H. Eaton 2018 Missouri University of Science and Technology

Neurocontroller Alternatives For "Fuzzy" Ball-And-Beam Systems With Nonuniform Nonlinear Friction, Danil V. Prokhorov, Donald C. Wunsch, Paul H. Eaton

Donald C. Wunsch

The ball-and-beam problem is a benchmark for testing control algorithms. Zadeh proposed (1994) a twist to the problem, which, he suggested, would require a fuzzy logic controller. This experiment uses a beam, partially covered with a sticky substance, increasing the difficulty of predicting the ball's motion. We complicated this problem even more by not using any information concerning the ball's velocity. Although it is common to use the first differences of the ball's consecutive positions as a measure of velocity and explicit input to the controller, we preferred to exploit recurrent neural networks, inputting only consecutive positions ...


Neurocontrol Of Turbogenerators With Adaptive Critic Designs, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley 2018 Missouri University of Science and Technology

Neurocontrol Of Turbogenerators With Adaptive Critic Designs, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Donald C. Wunsch

This paper presents the design of a neuro-controller for a turbogenerator using a novel technique based on adaptive critic designs (ACD). This adaptive critic design based neuro-controller augments/replaces the traditional automatic voltage regulator (AVR) and the turbine governor of the generator. Simulation results are presented to show that neural network controllers with the ACD have the potential to control turbogenerators when system conditions and configuration changes.


Neural Networks In The Former Soviet Union, Donald C. Wunsch 2018 Missouri University of Science and Technology

Neural Networks In The Former Soviet Union, Donald C. Wunsch

Donald C. Wunsch

No abstract provided.


Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow 2018 Missouri University of Science and Technology

Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow

Donald C. Wunsch

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 ...


Neural Network Enhancement Of The Los Alamos Force Deployment Estimator, Bobby Turner, Donald C. Wunsch 2018 Missouri University of Science and Technology

Neural Network Enhancement Of The Los Alamos Force Deployment Estimator, Bobby Turner, Donald C. Wunsch

Donald C. Wunsch

The Force Deployment Estimator (FDE) is a decision support system. It allocates transportation resources given inputs such as forces to be deployed and their desired arrival times. Other inputs are assumptions about conditions that affect performance: carrier start time, node capacity, sustainment shipping time, bulk sustainment per day, ammo sustainment per day, unit start time, carrier service time, carrier round trip time, and carrier reassignment time. Outputs include the mean and standard deviation of estimated unit arrival times versus goal times, and data files for post-processing. However, when a goal time is not met, the simulator gives no explanation of ...


Neural Network Demodulation For An Optical Sensor, Rohit Dua, Steve Eugene Watkins, Donald C. Wunsch 2018 Missouri University of Science and Technology

Neural Network Demodulation For An Optical Sensor, Rohit Dua, Steve Eugene Watkins, Donald C. Wunsch

Donald C. Wunsch

Methods and systems of neural network demodulation for an optical sensor. An optical sensor may be coupled to a structure and be capable of reflecting a reflected optical signal. A wavelength of the reflected optical signal may be spread based on a strain being applied to the structure. A replication device may receive the reflected optical signal from the optical sensor and produce a plurality of optical signals. A filter may be coupled to the replication device to receive an optical signal from the plurality of optical signals and filter the received optical signal. A detector may receive the filtered ...


Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani 2018 Missouri University of Science and Technology

Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani

Donald C. Wunsch

This paper presents a neural network (NN) based decentralized excitation controller design for large scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem controllers can be guaranteed. NNs are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded (UUB). Simulation results with a 3-machine power system demonstrate ...


Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes 2018 Missouri University of Science and Technology

Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes

Donald C. Wunsch

This paper presents a suite of neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control inputs are calculated using local signals, the transient and overall system stability can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system dynamics and the inter-connection terms, thus the requirements for exact system parameters are relaxed. Simulation studies with a three-machine power system demonstrate the effectiveness of the proposed controller designs.


Negative Reinforcement And Backtrack-Points For Recurrent Neural Networks For Cost-Based Abduction, Donald C. Wunsch, Ashraf M. Abdelbar, M. A. El-Hemaly, Emad A. M. Andrews 2018 Missouri University of Science and Technology

Negative Reinforcement And Backtrack-Points For Recurrent Neural Networks For Cost-Based Abduction, Donald C. Wunsch, Ashraf M. Abdelbar, M. A. El-Hemaly, Emad A. M. Andrews

Donald C. Wunsch

Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CKA) is an AI formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In this paper, we introduce two techniques for improving the performance of high order recurrent ...


Multiclass Cancer Classification Using Semisupervised Ellipsoid Artmap And Particle Swarm Optimization With Gene Expression Data, Georgios C. Anagnostopoulos, Donald C. Wunsch, Rui Xu 2018 Missouri University of Science and Technology

Multiclass Cancer Classification Using Semisupervised Ellipsoid Artmap And Particle Swarm Optimization With Gene Expression Data, Georgios C. Anagnostopoulos, Donald C. Wunsch, Rui Xu

Donald C. Wunsch

It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a tumor. with the emergence and rapid advancement of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to research on binary classification such as normal versus tumor samples, which attracts numerous efforts from a variety of disciplines, the discrimination of multiple tumor types is also important. Meanwhile, the selection of genes which are relevant to a certain cancer type not only improves the performance of the classifiers, but also provides ...


Multi-Class Cancer Classification By Semi-Supervised Ellipsoid Artmap With Gene Expression Data, Rui Xu, Donald C. Wunsch, Georgios C. Anagnostopoulos 2018 Missouri University of Science and Technology

Multi-Class Cancer Classification By Semi-Supervised Ellipsoid Artmap With Gene Expression Data, Rui Xu, Donald C. Wunsch, Georgios C. Anagnostopoulos

Donald C. Wunsch

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 already become a promising means for cancer classification. In addition to binary classification, the discrimination of multiple tumor types is also important semi-supervised ellipsoid ARTMAP (ssEAM) is a novel neural network architecture rooted in adaptive resonance theory suitable for classification tasks. ssEAM can achieve fast, stable and finite learning and create hyper-ellipsoidal clusters inducing complex nonlinear decision boundaries. Here, we demonstrate the capability of ssEAM to discriminate ...


Modified Cellular Simultaneous Recurrent Networks With Cellular Particle Swarm Optimization, Tae-Hyung Kim, Donald C. Wunsch 2018 Missouri University of Science and Technology

Modified Cellular Simultaneous Recurrent Networks With Cellular Particle Swarm Optimization, Tae-Hyung Kim, Donald C. Wunsch

Donald C. Wunsch

A cellular simultaneous recurrent network (CSRN) [1-11] is a neural network architecture that uses conventional simultaneous recurrent networks (SRNs), or cells in a cellular structure. The cellular structure adds complexity, so the training of CSRNs is far more challenging than that of conventional SRNs. Computer Go serves as an excellent test bed for CSRNs because of its clear-cut objective. For the training data, we developed an accurate theoretical foundation and game tree for the 2x2 game board. The conventional CSRN architecture suffers from the multi-valued function problem; our modified CSRN architecture overcomes the problem by employing ternary coding of the ...


Maximum Likelihood Methods In Biology Revisited With Tools Of Computational Intelligence, John E. Seiffertt IV, Andrew Vanbrunt, Donald C. Wunsch 2018 Missouri University of Science and Technology

Maximum Likelihood Methods In Biology Revisited With Tools Of Computational Intelligence, John E. Seiffertt Iv, Andrew Vanbrunt, Donald C. Wunsch

Donald C. Wunsch

We investigate the problem of identification of genes correlated with the occurrence of diseases in a given population. The classical method of parametric linkage analysis is combined with newer tools and results are achieved on a model problem. This traditional method has advantages over non-parametric methods, but these advantages have been difficult to realize due to their high computational cost. We study a class of Evolutionary Algorithms from the Computational Intelligence literature which are designed to cut such costs considerably for optimization problems. We outline the details of this algorithm, called Particle Swarm Optimization, and present all the equations and ...


Intelligent Strain Sensing On A Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors And Neural Networks, Kakkattukuzhy M. Isaac, Donald C. Wunsch, Steve Eugene Watkins, Rohit Dua, V. M. Eller 2018 Missouri University of Science and Technology

Intelligent Strain Sensing On A Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors And Neural Networks, Kakkattukuzhy M. Isaac, Donald C. Wunsch, Steve Eugene Watkins, Rohit Dua, V. M. Eller

Donald C. Wunsch

Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This ...


Intelligent Control Of Turbogenerator Exciter/Turbine On The Electric Power Grid To Improve Power Generation And Stability, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch 2018 Missouri University of Science and Technology

Intelligent Control Of Turbogenerator Exciter/Turbine On The Electric Power Grid To Improve Power Generation And Stability, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch

Donald C. Wunsch

A review of the applications of intelligent control to replace/augment the conventional excitation and/or turbine control of turbogenerators on the electric power grid is presented in the paper. The intelligent controller designs are based on neural networks and adaptive critic designs (ACDs). The feedback variables are completely based on local measurements from the generators. Simulations and some practical laboratory implementations on a single-machine-infinite-bus and a three-machine power system demonstrate that intelligent controllers are much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. The safety ...


Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank 2018 Missouri University of Science and Technology

Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank

Donald C. Wunsch

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 ...


Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models, Rui Xu, Xiao Hu, Donald C. Wunsch 2018 Missouri University of Science and Technology

Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models, Rui Xu, Xiao Hu, Donald C. Wunsch

Donald C. Wunsch

Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian networks, dynamic Bayesian networks, and linear additive regulation models, have been used to explore the behaviors of regulatory networks. In this paper, we investigate the inference of genetic regulatory networks from time series gene expression in the framework of recurrent neural network model.


Implementation Of Adaptive Critic-Based Neurocontrollers For Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley 2018 Missouri University of Science and Technology

Implementation Of Adaptive Critic-Based Neurocontrollers For Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Donald C. Wunsch

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller's training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes ...


Image Recognition Systems With Permutative Coding, Ernst M. Kussul, Donald C. Wunsch, Tatiana N. Baidyk 2018 Missouri University of Science and Technology

Image Recognition Systems With Permutative Coding, Ernst M. Kussul, Donald C. Wunsch, Tatiana N. Baidyk

Donald C. Wunsch

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%.


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