Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The cascade-correlation learning architecture
Advances in neural information processing systems 2
MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
An explanation of reasoning neural networks
Mathematical and Computer Modelling: An International Journal
The softening learning procedure
Mathematical and Computer Modelling: An International Journal
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To develop an appropriate internal representation, a deterministic learning algorithm that can adjust not only weights but also the number of adopted hidden nodes is proposed. The key mechanisms are 1.(1) the recruiting mechanism that recruits proper extra hidden nodes, and 2.(2) the reasoning mechanism that prunes potentially irrelevant hidden nodes This learning algorithm can make use of external environmental clues to develop an internal representation appropriate for the required mapping. The encoding problem and the parity problem are used to demonstrate the performance of the proposed algorithm. The experimental results are clearly positive.