Algebraic geometrical methods for hierarchical learning machines
Neural Networks
Algebraic Analysis for Nonidentifiable Learning Machines
Neural Computation
Probabilistic Neural Network Based Method for Fault Diagnosis of Analog Circuits
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
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Proposes three ways of designing artificial neural networks based on a unified framework and uses them to develop new models. First, the authors show that artificial neural networks can be understood as probability density functions with parameters. Second, the authors propose three design methods for new models: a method for estimating the occurrence probability of the inputs, a method for estimating the variance of the outputs, and a method for estimating the simultaneous probability of inputs and outputs. Third, the authors design three new models using the proposed methods: a neural network with occurrence probability estimation, a neural network with output variance estimation, and a probability competition neural network. The authors' experimental results show that the proposed neural networks have important abilities in information processing; they can tell how often a given input occurs, how widely the outputs are distributed, and from what kinds of inputs a given output is inferred