Multilayer feedforward networks are universal approximators
Neural Networks
The cascade-correlation learning architecture
Advances in neural information processing systems 2
A Hybrid Training Algorithm for Feedforward Neural Networks
Neural Processing Letters
Variable step search algorithm for feedforward networks
Neurocomputing
A fuzzy neighborhood-based training algorithm for feedforward neural networks
Neural Computing and Applications
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
A hybrid linear/nonlinear training algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Two regularizers for recursive least squared algorithms in feedforward multilayered neural networks
IEEE Transactions on Neural Networks
A new pruning heuristic based on variance analysis of sensitivity information
IEEE Transactions on Neural Networks
Parallel growing and training of neural networks using output parallelism
IEEE Transactions on Neural Networks
Neighborhood based Levenberg-Marquardt algorithm for neural network training
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
A new class of quasi-Newtonian methods for optimal learning in MLP-networks
IEEE Transactions on Neural Networks
Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks
IEEE Transactions on Neural Networks
Convergence of gradient method with momentum for two-Layer feedforward neural networks
IEEE Transactions on Neural Networks
On Adaptive Learning Rate That Guarantees Convergence in Feedforward Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An accelerated learning algorithm for multilayer perceptron networks
IEEE Transactions on Neural Networks
Growing Self-Organizing Map with cross insert for mixed-type data clustering
Applied Soft Computing
Cross-document structural relationship identification using supervised machine learning
Applied Soft Computing
D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process
Applied Soft Computing
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This paper presents a novel hybrid algorithm for feedforward neural networks, called a self organizing map-based initialization for hybrid training based on a two stage learning approach. First stage, a structure learning scheme which includes adding hidden neurons is used to determine the network size. Second stage, a FN (fuzzy neighborhood)-based hybrid learning scheme which we have recently proposed is used to adjust the network parameters. In this approach the weights between input and hidden layers are firstly adjusted by Kohonen algorithm with fuzzy neighborhood, whereas the weights connecting hidden and output layers are adjusted using gradient descent method. Four simulation examples are provided to demonstrate the efficiency of the approach compared with other well-known and recently proposed learning methods.