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Full-Text Articles in Engineering
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.
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 …
Improving The Performance Of Particle Swarm Optimization Using Adaptive Critics Designs, Ganesh K. Venayagamoorthy, Sheetal Doctor
Improving The Performance Of Particle Swarm Optimization Using Adaptive Critics Designs, Ganesh K. Venayagamoorthy, Sheetal Doctor
Electrical and Computer Engineering Faculty Research & Creative Works
Swarm intelligence algorithms are based on natural behaviors. Particle swarm optimization (PSO) is a stochastic search and optimization tool. Changes in the PSO parameters, namely the inertia weight and the cognitive and social acceleration constants, affect the performance of the search process. This paper presents a novel method to dynamically change the values of these parameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing the values of the PSO parameters.
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.
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 …