| Summary: | 碩士 === 國立臺灣大學 === 資訊工程研究所 === 82 === The objective of this research is to propose methods on solving
the learning problems of multi-layer neural networks. The most
well-known and commonly used learning algorithm is Back
Propagation (BP) algorithm. There are three main drawbacks of
BP: 1. the slowness of the learning speed, 2. the convergence
to local minima, and 3. the absence of any theoretical result,
allowing for a priori determination of an optimal network
architecture for a given task. To solve these problems, we
propose three methods: The first is to initialize weights in
multi-layer quadratic sigmoid networks; The second is to learn
in successive residual space; The third is using the topology
preserving maps formmed in MLPs. These methods can be applied
to pattern recognition problems espically when the training
patterns have lined structure.
|
|---|
