Add to ORKGAbstract: A new neural network based optimization algorithm is proposed.The presented model is a discrete-time, continuous-stateHopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos andHopfield neural networks: particles learn fromtheir own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarmis guaranteed.The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems
To overcome the problem of premature convergence on Particle Swarm Optimization (PSO), this paper pr...
A novel neural network training algorithm based on particle swarm optimization (PSO) and optimal for...
After more than a decade of research, there now exist several neural-network techniques for solving ...
A single particle structure of particle swarm optimization was analyzed which is found to have some ...
Abstract: A new particle swarm optimization (PSO) algorithm having a chaotic Hopfield Neural Network...
Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network struct...
Abstract: A single particle structure of the classical particle swarm optimization was analyzed whic...
Swarm colonies reproduce social habits. Working together in a group to reach a predefined goal is a ...
Neural networks can be successfully applied to solving certain types of combinatorial optimization p...
Abstract: Multi-modal optimisation problems are characterised by the presence of either local sub-op...
Feed-forward networks are one of the most used neural networks in various domains because of their u...
Abstract—The control approach for chaotic systems is one of the hottest research topics in nonlinear...
Multilayer feed-forward artificial neural networks are one of the most frequently used data mining m...
Combinatorial optimization problems can be solved with the Hopfield Neural Network. If we choose con...
This paper proposes the Particle Swarm Optimization model for enhancing the performance of an Artifi...
To overcome the problem of premature convergence on Particle Swarm Optimization (PSO), this paper pr...
A novel neural network training algorithm based on particle swarm optimization (PSO) and optimal for...
After more than a decade of research, there now exist several neural-network techniques for solving ...
A single particle structure of particle swarm optimization was analyzed which is found to have some ...
Abstract: A new particle swarm optimization (PSO) algorithm having a chaotic Hopfield Neural Network...
Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network struct...
Abstract: A single particle structure of the classical particle swarm optimization was analyzed whic...
Swarm colonies reproduce social habits. Working together in a group to reach a predefined goal is a ...
Neural networks can be successfully applied to solving certain types of combinatorial optimization p...
Abstract: Multi-modal optimisation problems are characterised by the presence of either local sub-op...
Feed-forward networks are one of the most used neural networks in various domains because of their u...
Abstract—The control approach for chaotic systems is one of the hottest research topics in nonlinear...
Multilayer feed-forward artificial neural networks are one of the most frequently used data mining m...
Combinatorial optimization problems can be solved with the Hopfield Neural Network. If we choose con...
This paper proposes the Particle Swarm Optimization model for enhancing the performance of an Artifi...
To overcome the problem of premature convergence on Particle Swarm Optimization (PSO), this paper pr...
A novel neural network training algorithm based on particle swarm optimization (PSO) and optimal for...
After more than a decade of research, there now exist several neural-network techniques for solving ...