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.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Pattern Recognition in Control Chart Using Neural Network based on a New Statistical Feature

Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural netw...

متن کامل

A Real-Time Electroencephalography Classification in Emotion Assessment Based on Synthetic Statistical-Frequency Feature Extraction and Feature Selection

Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...

متن کامل

A Self-Organizing Neural Fuzzy Inference Network

A self-organizing neural network is proposed which is inherently a fuzzy inference system with the capability of learning fuzzy rules from data. The learning strategy consists of two phases: a self-organizing clustering to establish the structure of the network as well as the initial values of its parameters and a supervised learning phase for optimal adjustment of these parameters. After learn...

متن کامل

Optimization Of Neural Network Inputs By Feature Selection Methods

The main idea of this paper is to compare feature selection methods for dimension reduction of the original dataset to reach optimization of steganalysis process by artificial neural networks (ANN). Feature selection methods are tools based on statistic exploited in pre-processing step of data mining workflow. These methods are very useful in a dimension reduction, removing of insignificant dat...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Electronics

سال: 2023

ISSN: ['2079-9292']

DOI: https://doi.org/10.3390/electronics12102281