A Neural Network Based on the Johnson S U Translation System and Related Application to Electromyogram Classification
Autor: | Toshio Tsuji, Taro Shibanoki, Hideaki Hayashi |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Hyperparameter
electromyography General Computer Science Artificial neural network Johnson distribution business.industry Computer science Posterior probability pattern recognition General Engineering Biomedical signal processing Pattern recognition neural networks TK1-9971 Normal distribution Discriminative model Hyperparameter optimization Kurtosis General Materials Science Artificial intelligence Electrical engineering. Electronics. Nuclear engineering Linear combination business |
Zdroj: | IEEE Access, Vol 9, Pp 154304-154317 (2021) |
ISSN: | 2169-3536 |
Popis: | Electromyogram (EMG) classification is a key technique in EMG-based control systems. Existing EMG classification methods, which do not consider EMG features that have distribution with skewness and kurtosis, have limitations such as the requirement to tune hyperparameters. In this paper, we propose a neural network based on the Johnson $S_{\mathrm {U}}$ translation system that is capable of representing distributions with skewness and kurtosis. The Johnson system is a normalizing translation that transforms non-normal distribution data into normal distribution data, thereby enabling the representation of a wide range of distributions. In this study, a discriminative model based on the multivariate Johnson $S_{\mathrm {U}}$ translation system is transformed into a linear combination of coefficients and input vectors using log-linearization; then, it is incorporated into a neural network structure. This allows the calculation of the posterior probability of each class given the input vectors and the determination of model parameters as weight coefficients of the network. The uniqueness of convergence of the network learning is theoretically guaranteed. In the experiments, the suitability of the proposed network for distributions including skewness and kurtosis was evaluated using artificially generated data. Its applicability to real biological data was also evaluated via EMG classification experiments. The results showed that the proposed network achieved high classification performance (e.g., 99.973% accuracy using Khushaba’s dataset) without the need for hyperparameter optimization. |
Databáze: | OpenAIRE |
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