Multilayer feedforward networks are universal approximators
Neural Networks
Analysis of neural networks with redundancy
Neural Computation
Introduction to artificial neural systems
Introduction to artificial neural systems
Improving the convergence of the back-propagation algorithm
Neural Networks
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
Adaptive Control
Tabu Search
Artificial Neural Networks: Concepts and Theory
Artificial Neural Networks: Concepts and Theory
A unified approach on fast training of feedforward and recurrentnetworks using EM algorithm
IEEE Transactions on Signal Processing
A generalized learning paradigm exploiting the structure of feedforward neural networks
IEEE Transactions on Neural Networks
Advanced neural-network training algorithm with reduced complexity based on Jacobian deficiency
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Stochastic optimization algorithm with probability vector
CI'10 Proceedings of the 4th WSEAS international conference on Computational intelligence
WSEAS Transactions on Information Science and Applications
A new architecture selection method based on tabu search for artificial neural networks
Expert Systems with Applications: An International Journal
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This paper presents a novel neural network learning algorithm, the tabu-based neural network learning algorithm (TBBP). In our work, the TBBP mainly use the tabu search (TS) to improve the nonlinear function approximating ability of the neural network. By using the TS in the global search, the algorithm can escape from the local minima and obtain some superior global solutions, the weights of the neural network, to approximate the nonlinear function. Results confirm that the TBBP can greatly improve the approximating ability of the neural network for several typical nonlinear functions.