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Full-Text Articles in Engineering
Navigation Of Mobile Sensors Using Pso And Embedded Pso In A Fuzzy Logic Controller, Ganesh K. Venayagamoorthy, Sheetal Doctor
Navigation Of Mobile Sensors Using Pso And Embedded Pso In A Fuzzy Logic Controller, Ganesh K. Venayagamoorthy, Sheetal Doctor
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents novel structures for optimization and communication of a swarm of mobile sensors or robots for maximizing local and global tasks such as firefighting, landmine detection, radioactivity detection, etc. The navigation of the sensors is carried out using two strategies. The first strategy is based on particle swarm optimization (PSO) and the second strategy is based on a swarm of fuzzy logic based controllers. In addition, the membership functions and the rules of the fuzzy logic controller (FLC) are optimized using the PSO algorithm. Navigation of mobile sensors is considered in this paper to locate desirable target sources …
Time Series Prediction With Recurrent Neural Networks Using A Hybrid Pso-Ea Algorithm, Nian Zhang, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Xindi Cai
Time Series Prediction With Recurrent Neural Networks Using A Hybrid Pso-Ea Algorithm, Nian Zhang, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Xindi Cai
Electrical and Computer Engineering Faculty Research & Creative Works
To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks 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 …
Optimal Pso For Collective Robotic Search Applications, Ganesh K. Venayagamoorthy, Venu Gopal Gudise, Sheetal Doctor
Optimal Pso For Collective Robotic Search Applications, Ganesh K. Venayagamoorthy, Venu Gopal Gudise, Sheetal Doctor
Electrical and Computer Engineering Faculty Research & Creative Works
Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This work presents the application of particle swarm optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches.
Fpga Placement And Routing Using Particle Swarm Optimization, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Fpga Placement And Routing Using Particle Swarm Optimization, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Electrical and Computer Engineering Faculty Research & Creative Works
Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. One of the necessary requirements to effectively utilize the FPGA's fixed resources is an efficient placement and routing mechanism. This paper presents particle swarm optimization (PSO) for FPGA placement and routing. Preliminary results for the implementation of an arithmetic logic unit on a Xilinx FPGA show that PSO is a potential technique for solving the placement and routing problem.
Comparison Of Particle Swarm Optimization And Backpropagation As Training Algorithms For Neural Networks, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Comparison Of Particle Swarm Optimization And Backpropagation As Training Algorithms For Neural Networks, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Electrical and Computer Engineering Faculty Research & Creative Works
Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP …
Evolving Digital Circuits Using Particle Swarm, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Evolving Digital Circuits Using Particle Swarm, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Electrical and Computer Engineering Faculty Research & Creative Works
Particle swarm optimization (PSO) motivated by the social behavior of organisms is proposed for evolution of combinational logic circuits. Results are presented to show that PSO based evolution of digital circuits are equivalent to or even with better solutions (with minimum number of logic gates) than that of a human designer and other genetic algorithm (GA) based techniques. This PSO based approach converges faster than other approaches reported in literature using genetic algorithms and as a result the computational intensity involved in hardware evolution is reduced. Examples taken from the literature are used to evaluate the performance of the proposed …