Abstract
A new model of minimal uncertainty neural networks (MUNN) is proposed in this article. The model is based on the Minimal Uncertainty Adjudgment to construct the structure, and it combines with Bayesian Theorem and Particle Swarm Optimization (PSO) for training. The model can determine the parameters of neural networks rapidly and efficiently. The effectiveness of the algorithm is demonstrated through the classification of the taste signals of 10 kinds of tea. The simulated results show its feasibility and validity.
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Wang, Y., Zhou, CG., Huang, YX., Feng, XY. (2004). Training Minimal Uncertainty Neural Networks by Bayesian Theorem and Particle Swarm Optimization. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_89
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DOI: https://doi.org/10.1007/978-3-540-30499-9_89
Publisher Name: Springer, Berlin, Heidelberg
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