A HYBRID LEARNING NEURAL-NETWORK ARCHITECTURE WITH LOCALLY ACTIVATED HIDDEN LAYER FOR FAST AND ACCURATE MAPPING

Abstract

A hybrid learning neural network architecture based on global clustering and local learning is developed that not only speeds up learning but also enhances mapping accuracy for both real-valued and logic functions. This fast learning while satisfying a prescribed accuracy is an essential characteristic for real-time on-line learning systems. Either Kohonen's self-organizing feature map or the leader clustering algorithm is used to partition the input data space for local learning. The input data selects a subset of hidden nodes (either sigmoidal or Gaussian) that contribute to the output calculation. Example results demonstrate the proposed architecture's superior convergence properties over the original backpropagation network or its improvement techniques.