ACCELERATING TRAINING OF FEEDFORWARD NEURAL NETWORKS
Carl G. Looney · International Journal of Artificial Intelligence Tools · 1994
We review methods and techniques for training feedforward neural networks that avoid problematic behavior, accelerate the convergence, and verify the training. Adaptive step gain, bipolar activation functions, and conjugate gradients are powerful stabilizers. Random search techniques circumvent the local minimum trap and avoid specialization due to overtraining. Testing assures quality learning.