Dynamic Learning for Forward Neural Networks
Ustc Beijing · Dianzi xuebao · 1998
Vectors, which consist of the output of every neuron in same hidden layer corresponding to different samples, should be nonlinearly correlated. With this basic fact, this Paper firstly gives the definition of linearly correlated vector and corresponding nonlinear correlation measure for every hidden layer, then adds or deletes a neuron for a hidden layer according to its nonlinear correlation measure and adjusts the neural networks weight, values appropriately. This method can not only avoid confining the number of neuron units in a hidden layer, but also escape local minimum during the learnig process. According to error analysis in detail, if gives the optimistic rule of deleting neuron in hidden layer. Numerical experiments illustrate its efficiency.