Authors:
Seiya Satoh
and
Ryohei Nakano
Affiliation:
Chubu University, Japan
Keyword(s):
Complex-valued Multilayer Perceptron, Learning Method, Singular Region, Search Pruning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
In the search space of a complex-valued multilayer perceptron having J hidden units, C-MLP(J), there are singular regions, where the gradient is zero. Although singular regions cause serious stagnation of learning, there exist narrow descending paths from the regions. Based on this observation, a completely new learning method called C-SSF (complex singularity stairs following) 1.0 was proposed, which utilizes singular regions to generate starting points of C-MLP(J) search. Although C-SSF1.0 finds excellent solutions of successive C-MLPs, it takes long CPU time because the number of searches increases as J gets larger. To deal with this problem, C-SSF1.1 was proposed, a few times faster by the introduction of search pruning, but it still remained unsatisfactory. In this paper we propose a yet faster C-SSF1.3, going further with search pruning, and then evaluate the method in terms of solution quality and processing time.