Modeling with constructive backpropagation
Neural Networks
A pruning method for the recursive least squared algorithm
Neural Networks
Feedforward Neural Network Construction Using Cross Validation
Neural Computation
Adaptive growing-and-pruning neural network control for a linear piezoelectric ceramic motor
Engineering Applications of Artificial Intelligence
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive dissolved oxygen control based on dynamic structure neural network
Applied Soft Computing
Self-organization of topographic bilinear networks for invariant recognition
Neural Computation
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Constructive feedforward neural networks using Hermite polynomial activation functions
IEEE Transactions on Neural Networks
A node pruning algorithm based on a Fourier amplitude sensitivity test method
IEEE Transactions on Neural Networks
A New Jacobian Matrix for Optimal Learning of Single-Layer Neural Networks
IEEE Transactions on Neural Networks
Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation
IEEE Transactions on Neural Networks
A Constrained Optimization Approach to Preserving Prior Knowledge During Incremental Training
IEEE Transactions on Neural Networks
Guiding Hidden Layer Representations for Improved Rule Extraction From Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Identification of Extended Hammerstein Systems Using Dynamic Self-Optimizing Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Adaptive Evolutionary Artificial Neural Networks for Pattern Classification
IEEE Transactions on Neural Networks
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It has been shown extensively that the dynamic behaviors of a neural system are strongly influenced by the network architecture and learning process. To establish an artificial neural network (ANN) with self-organizing architecture and suitable learning algorithm for nonlinear system modeling, an automatic axon-neural network (AANN) is investigated in the following respects. First, the network architecture is constructed automatically to change both the number of hidden neurons and topologies of the neural network during the training process. The approach introduced in adaptive connecting-and-pruning algorithm (ACP) is a type of mixed mode operation, which is equivalent to pruning or adding the connecting of the neurons, as well as inserting some required neurons directly. Secondly, the weights are adjusted, using a feedforward computation (FC) to obtain the information for the gradient during learning computation. Unlike most of the previous studies, AANN is able to self-organize the architecture and weights, and to improve the network performances. Also, the proposed AANN has been tested on a number of benchmark problems, ranging from nonlinear function approximating to nonlinear systems modeling. The experimental results show that AANN can have better performances than that of some existing neural networks.