Multi-Valued Neuron with Sigmoid Activation Function
This paper explores Multi-Valued Neurons (MVN) enhanced with Sigmoid Activation Functions (MVN-sig). It discusses the motivation behind incorporating sigmoid functions, detailing the expected improvements in performance and tolerance despite increased execution time. The work presents learning methodologies utilizing the back-propagation rule and showcases simulation results using benchmark datasets like the Wine and Glass identification datasets, achieving 100% training accuracy. Additionally, the document outlines current limitations and suggests directions for future research in MVN applications.
Multi-Valued Neuron with Sigmoid Activation Function
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Presentation Transcript
Multi-Valued Neuron with Sigmoid Activation Function Shin-Fu Wu 2013/5/10
Outlines • Multi-Valued Neuron (MVN) • MVN with Sigmoid Activation Function (MVN-sig) • Motivation and Expectation • Multi-Valued Sigmoid Activation Function • Learning using Back-propagation Rule • Simulation Results • Benchmark simulations • Problems and Limitations • Movement of Weighted Sum • Future Works
MVN-sig • Motivation and Expectation • Basic idea: approach the functionality of MVN using sigmoid function • Differentiable • Multi-Valued Logic • Expectation: • Better performance and tolerance • More execution time
Simulation Results • Benchmark Simulations • Wine dataset (5-fold CV, 100% trained)
Simulation Results • Glass identification dataset (5-fold CV, 100% trained)
