scholarly journals Improving Adaptive Learning Rate With Backpropogation on Retail Rice Price Prediction in Traditional Markets

2019 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Erwin Binsar Hamonangan Ompusunggu ◽  
Solikhun Solikhun ◽  
Iin Parlina ◽  
Sumarno Sumarno ◽  
Indra Gunawan
Keyword(s):  
Adaptive Learning ◽  
Retail Price ◽  
Learning Rate ◽  
Traditional Markets ◽  
Adaptive Learning Rate ◽  
Architectural Models ◽  
Rice Prices ◽  
The Government ◽  
The Stability ◽  
Accuracy Data

Rice is the most important staple food and carbohydrate food in the world especially people in Indonesia. This study aims to predict the retail price of rice in traditional markets using backpropogation by improvising Adaptive Learning Rate to increase the value of accuracy. Data sources were obtained from the Central Statistics Agency (BPS) in 33 provinces in Indonesia for the retail price of rice in the traditional market (Rupiah / kg) for the past 6 years (2011-2016). The results of the study state that the improvised learning rate uses 2 models: 2-10-1 and 2-15-1 (LR= 0,1; 0,5; 0,9) that the best architectural models are 4-15-1 (LR= 0.9) with an accuracy of 82%, Training MSE 0,000999936, Testing MSE 0.016051433 and Epoch 20515. The results of this study are expected to provide input to the government in providing input on predictions of retail rice prices that have an impact on the stability of rice prices in Indonesia.

scholarly journals Stability Analysis of Neural Networks-Based System Identification

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Talel Korkobi ◽  
Mohamed Djemel ◽  
Mohamed Chtourou
Keyword(s):  
Stability Analysis ◽  
Adaptive Learning ◽  
Learning Rule ◽  
Stability Domain ◽  
Learning Rate ◽  
Training Algorithm ◽  
Adaptive Learning Rate ◽  
Systems Identification ◽  
The Stability ◽  
Convergence Stability

This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

Performance-Based Adaptive Learning Rate Scheduler Algorithm

2021 ◽  
pp. 417-433
Author(s):  
Vakada Naveen ◽  
Yaswanth Mareedu ◽  
Neeharika Sai Mandava ◽  
Sravya Kaveti ◽  
G. Krishna Kishore
Keyword(s):  
Adaptive Learning ◽  
Learning Rate ◽  
Adaptive Learning Rate

Correlation filter tracking based on adaptive learning rate and location refiner

2018 ◽  
Vol 26 (8) ◽  
pp. 2100-2111 ◽  
Author(s):  
刘教民 LIU Jiao-min ◽  
郭剑威 GUO Jian-wei ◽  
师 硕 SHI Shuo
Keyword(s):  
Adaptive Learning ◽  
Learning Rate ◽  
Correlation Filter ◽  
Adaptive Learning Rate

MEMS Inertial Sensor Fault Diagnosis Using a CNN-Based Data-Driven Method

2020 ◽  
Vol 34 (14) ◽  
pp. 2059048
Author(s):  
Tong Gao ◽  
Wei Sheng ◽  
Mingliang Zhou ◽  
Bin Fang ◽  
Liping Zheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Optimization Method ◽  
Learning Rate ◽  
Data Driven ◽  
Adaptive Learning Rate ◽  
Improved Performance ◽  
Rate Optimization

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

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