scholarly journals Data-driven Learning Algorithm of Neural Fuzzy Based Hammerstein-Wiener System

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Feng Li ◽  
Yinsheng Luo ◽  
Naibao He ◽  
Ya Gu ◽  
Qingfeng Cao
Keyword(s):  
Finite Impulse Response ◽  
Learning Algorithm ◽  
Gradient Algorithm ◽  
Data Driven ◽  
Noise Model ◽  
Learning Approach ◽  
Learning Technology ◽  
Wiener System ◽  
Neural Fuzzy ◽  
Linear Block

A novel data-driven learning approach of nonlinear system represented by neural fuzzy Hammerstein-Wiener model is presented. The Hammerstein-Wiener system has two static nonlinear blocks represented by two independent neural fuzzy models surrounding a dynamic linear block described by finite impulse response model. The multisignal theory is designed for employing Hammerstein-Wiener system to separate parameter learning issues. To begin with, the output nonlinearity parameters are learned utilizing separable signal with different amplitudes. Furthermore, correlation analysis method is implemented for estimating linear block parameters using separable signal inputs and outputs; thereby, the interference of process noise is effectively handled. Finally, multi-innovation learning technology is introduced to improve system learning accuracy, and then, multi-innovation extended stochastic gradient algorithm is obtained for optimizing input nonlinearity and noise model using multi-innovation technique and gradient search method. The simulation results display that presented data-driven learning approach has the availability of learning Hammerstein-Wiener system.

scholarly journals Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuquan Chen ◽  
Hongxing Wang ◽  
Jie Shen ◽  
Xingwei Zhang ◽  
Xiaowei Gao
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Target Detection ◽  
Defect Detection ◽  
Learning Algorithm ◽  
Small Sample ◽  
Iterative Learning ◽  
Data Driven ◽  
Learning Technology ◽  
Line Defect

Deep learning technology has received extensive consideration in recent years, and its application value in target detection is also increasing day by day. In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.

An Expert Data-Driven Air Combat Maneuver Model Learning Approach

2021 ◽  
Author(s):  
Su-Jeong Park ◽  
Soon-Seo Park ◽  
Han-Lim Choi ◽  
Kyeong-Soo An ◽  
Young-Gon Kim
Keyword(s):  
Data Driven ◽  
Learning Approach ◽  
Model Learning ◽  
Air Combat ◽  
Expert Data

Decentralized Opportunistic Channel Access in CRNs Using Big-Data Driven Learning Algorithm

2021 ◽  
Vol 5 (1) ◽  
pp. 57-69
Author(s):  
Zuohong Xu ◽  
Zhou Zhang ◽  
Shilian Wang ◽  
Alireza Jolfaei ◽  
Ali Kashif Bashir ◽  
...  
Keyword(s):  
Big Data ◽  
Learning Algorithm ◽  
Data Driven ◽  
Channel Access ◽  
Opportunistic Channel Access

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

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