Multilayer feedforward networks are universal approximators
Neural Networks
Introduction to Grey system theory
The Journal of Grey System
Creating artificial neural networks that generalize
Neural Networks
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
AG-ART: An adaptive approach to evolving ART architectures
Neurocomputing
Neuro-immune approach to solve routing problems
Neurocomputing
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Seasonality and neural networks: a new approach
International Journal of Metaheuristics
On the role of population size and niche radius in fitness sharing
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
A new pruning heuristic based on variance analysis of sensitivity information
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Neural-network construction and selection in nonlinear modeling
IEEE Transactions on Neural Networks
Partially connected feedforward neural networks structured by input types
IEEE Transactions on Neural Networks
Mutation-based genetic neural network
IEEE Transactions on Neural Networks
An Optimization Methodology for Neural Network Weights and Architectures
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Hybrid Training Method for MLP: Optimization of Architecture and Training
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We propose a method for designing artificial neural networks (ANNs) for prediction problems based on an evolutionary constructive and pruning algorithm (ECPA). The proposed ECPA begins with a set of ANNs with the simplest possible structure, one hidden neuron connected to an input node, and employs crossover and mutation operators to increase the complexity of an ANN population. Additionally, cluster-based pruning (CBP) and age-based survival selection (ABSS) are proposed as two new operators for ANN pruning. The CBP operator retains significant neurons and prunes insignificant neurons on a probability basis and therefore prevents the exponential growth of an ANN. The ABSS operator can delete old ANNs with potentially complex structures and then introduce new ANNs with simple structures; thus, the ANNs are less likely to be trapped in a fully connected topology. The ECPA framework incorporates constructive and pruning approaches in an attempt to efficiently evolve compact ANNs. As a demonstration of the method, ECPA is applied to three prediction problems: the Mackey-Glass time series, the number of sunspots, and traffic flow. The numerical results show that ECPA makes the design of ANNs more feasible and practical for real-world applications.