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Comparative Application Of Differential Evolution And Particle Swarm Techniques To Reactive Power And Voltage Control, G. A. Bakare, G. Krost, Ganesh K. Venayagamoorthy, U. O. Aliyu
Comparative Application Of Differential Evolution And Particle Swarm Techniques To Reactive Power And Voltage Control, G. A. Bakare, G. Krost, Ganesh K. Venayagamoorthy, U. O. Aliyu
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the comparative application of two metaheuristic approaches: Differential Evolution (DE) and Particle Swarm Optimization (PSO) to the solution of the reactive power and voltage control problem. Efficient distribution of reactive power in an electric network leads to minimization of the system losses and improvement of the system voltage profile. It can be achieved by varying the excitation of generators or the on-load tap changer positions of transformers as well as by switching of discrete portions of inductors or capacitors etc. This constitutes a typical mixed integer non-linear optimization problem for the solution of which metaheuristic techniques have …
Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank
Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank
Electrical and Computer Engineering Faculty Research & Creative Works
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 …
Miso Damping Controller Design For A Tcsc Using Particle Swarm, Swakshar Ray, Ganesh K. Venayagamoorthy, Balarko Chaudhuri, Rajat Majumder
Miso Damping Controller Design For A Tcsc Using Particle Swarm, Swakshar Ray, Ganesh K. Venayagamoorthy, Balarko Chaudhuri, Rajat Majumder
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a new approach for designing multi-input-single-output (MISO) damping controller for a TCSC in a multi-machine power system. The damping controller design uses particle swarm optimization (PSO) to determine the coefficients of single or multi-stage lead-lag compensators. The classical technique works well in the design of lead-lag compensators for SISO controllers. But, there is no proper step-by-step procedure to achieve the desired performance characteristics for a MISO controller. Hence, in this paper, a computational optimization tool has been used to determine the optimal gains and time constants of a linear MISO damping controller. The damping controller is implemented …
Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow
Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow
Engineering Management and Systems Engineering Faculty Research & Creative Works
Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly …
Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha
Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a companding nonuniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs and particle swarm optimization, aiming to maximize the signal-to-noise ratio. The comparison of these optimal quantizer designs over a bit-rate range of 3-6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union's Perceptual Evaluation of Speech Quality standard
Multiclass Cancer Classification Using Semisupervised Ellipsoid Artmap And Particle Swarm Optimization With Gene Expression Data, Georgios C. Anagnostopoulos, Donald C. Wunsch, Rui Xu
Multiclass Cancer Classification Using Semisupervised Ellipsoid Artmap And Particle Swarm Optimization With Gene Expression Data, Georgios C. Anagnostopoulos, Donald C. Wunsch, Rui Xu
Electrical and Computer Engineering Faculty Research & Creative Works
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 …