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Evolving Combinational Logic Circuits Using A Hybrid Quantum Evolution And Particle Swarm Inspired Algorithm, Phillip W. Moore, Ganesh K. Venayagamoorthy
Evolving Combinational Logic Circuits Using A Hybrid Quantum Evolution And Particle Swarm Inspired Algorithm, Phillip W. Moore, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, an algorithm inspired from quantum evolution and particle swarm to evolve combinational logic circuits is presented. This algorithm uses the framework of the local version of particle swarm optimization with quantum evolutionary algorithms, and integer encoding. A multi-objective fitness function is used to evolve the combinational logic circuits in order obtain feasible circuits with minimal number of gates in the design. A comparative study indicates the superior performance of the hybrid quantum evolution-particle swarm inspired algorithm over the particle swarm and other evolutionary algorithms (such as genetic algorithms) independently.
Comparison Of Non-Uniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Wenwei Zha, Ganesh K. Venayagamoorthy
Comparison Of Non-Uniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Wenwei Zha, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a companding non-uniform 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 (ACD) and particle swarm optimization (PSO), aiming to maximize the signal to noise ratio (SNR). The comparison of these optimal quantizer designs over bit rate range of 3 to 6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union''s Perceptual …
Fuzzy Pso: A Generalization Of Particle Swarm Optimization, S. Abdelshahid, Donald C. Wunsch, Ashraf M. Abdelbar
Fuzzy Pso: A Generalization Of Particle Swarm Optimization, S. Abdelshahid, Donald C. Wunsch, Ashraf M. Abdelbar
Electrical and Computer Engineering Faculty Research & Creative Works
In standard particle swarm optimization (PSO), the best particle in each neighborhood exerts its influence over other particles in the neighborhood. In this paper, we propose fuzzy PSO, a generalization which differs from standard PSO in the following respect: charisma is defined to be a fuzzy variable, and more than one particle in each neighborhood can have a non-zero degree of charisma, and, consequently, is allowed to influence others to a degree that depends on its charisma. We evaluate our model on the weighted maximum satisfiability (maxsat) problem, comparing performance to standard PSO and to Walk-Sat.
Gene Regulatory Networks Inference With Recurrent Neural Network Models, Rui Xu, Donald C. Wunsch
Gene Regulatory Networks Inference With Recurrent Neural Network Models, Rui Xu, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
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 …
Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch
Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
We applied an architecture which automates the design of simultaneous recurrent network (SRN) 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 simultaneous recurrent network for the engine data classification. The experimental results show that our approach gives …
A Comparison Of Pso And Backpropagation For Training Rbf Neural Networks For Identification Of A Power System With Statcom, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
A Comparison Of Pso And Backpropagation For Training Rbf Neural Networks For Identification Of A Power System With Statcom, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.
Neural Networks Based Non-Uniform Scalar Quantizer Design With Particle Swarm Optimization, Wenwei Zha, Ganesh K. Venayagamoorthy
Neural Networks Based Non-Uniform Scalar Quantizer Design With Particle Swarm Optimization, Wenwei Zha, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Quantization is a crucial link in the process of digital speech communication. Non-uniform quantizer such as the logarithm quantizers are commonly used in practice. In this paper, a companding non-uniform quantizer is designed using two neural networks to perform the nonlinear transformation. Particle swarm optimization is applied to find the weights of neural networks such that the signal to noise ratio (SNR) is maximized. Simulation results on different speech samples are presented and the proposed quantizer design is compared with the logarithm quantizer for bit rates ranging from 3 to 8.
Optimal Control Parameters For A Upfc In A Multimachine Using Pso, Ganesh K. Venayagamoorthy
Optimal Control Parameters For A Upfc In A Multimachine Using Pso, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
The crucial factor affecting the modern power systems today is load flow control. The unified power flow controller (UPFC) is an effective means for controlling the power flow and can provide damping capability during transient conditions. The UPFC is controlled conventionally using PI controllers. The optimal design of the PI controllers for a UPFC is a challenging task and time consuming using the conventional techniques. This paper presents an approach using particle swarm optimization (PSO) for the design of optimal conventional controllers for a UPFC in a multimachine power system. Simulation results are presented to show the effectiveness of the …