Multilayer feedforward networks are universal approximators
Neural Networks
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Evolutionary Learning of Modular Neural Networks withGenetic Programming
Applied Intelligence
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Adaptive image interpolation using probabilistic neural network
Expert Systems with Applications: An International Journal
Pattern recognition using neural-fuzzy networks based on improved particle swam optimization
Expert Systems with Applications: An International Journal
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
IEEE Transactions on Neural Networks
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
In this paper, a cooperative binary-real particle swarm optimization is applied to tune the structure and parameters of a neural network. A neural network with switches of its links, which is used to decide whether there is a link between two neurons or not, is introduced firstly. Thus, the structure of a neural network can be decided by the switches. A cooperative binary-real particle swarm optimization algorithm is utilized to find the compact structures and optimal parameters of the proposed neural network. The number of hidden nodes of the neural network is increased from a small number until its learning ability is achieved. The simulation experiments indicate that the proposed approach can obtain better results than the existing approaches in recent literature.