Designing a Fuzzy Adaptive Neural Network Leveraging a Feature Self-Enhancement Unit and Statistical Selection Methods
نویسندگان
چکیده
In this study, we propose an advanced category of a fuzzy adaptive neural network (FANN) based on feature self-enhancement unit (FSU) and statistical selection methods (SSMs). Undoubtedly, the raw data contain large amount information with varying importance. One most important tasks for regression model design is to avoid losing these details. However, cannot participate in whole training process due fuzzification structure conventional networks (FNNs). Meanwhile, polynomial-based neuron also has its limitations as common node FNNs. For example, polynomial neuron, complexity neurons increases exponentially increase size. Consequently, overfitting insufficient are two primary drawbacks To address limitations, designed FSU SSM effective vehicles reduce dimensionality select significant information. The proposed FANN demonstrates capability improve modeling accuracy networks. Moreover, first instance integrating techniques into model. validate showcase superiority FANN, applied 16 machine learning datasets, outperforming other comparative models 81.25% datasets utilized. Additionally, outperformed latest FNN models, achieving average 5.1% accuracy. comparison experiment section not only includes classical but references experimental results from recent related studies.
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ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12102281