scholarly journals Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor

2021 ◽  
Vol 21 (1) ◽  
pp. 48
Author(s):  
Widi Aribowo ◽  
Supari Muslim ◽  
Fendi Achmad ◽  
Aditya Chandra Hermawan
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Control Method ◽  
Absolute Error ◽  
Dc Motor ◽  
Motor Speed ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Control Approach ◽  
Nature Inspired Algorithm

This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method is compared with the Proportional Integral Derivative (PID) method, the Feed Forward Neural Network (FFNN), and the Cascade Forward Backpropagation Neural Network (CFBNN). From the results of the study, the proposed control method has good capabilities compared to standard neural methods, namely FFNN and CFBNN. Integral Time Absolute Error and Square Error (ITAE and ITSE) values from the proposed method are on average of 0.96% and 0.2% better than the FFNN and CFBNN methods.

scholarly journals Improving neural network using a sine tree-seed algorithm for tuning motor DC

2021 ◽  
Vol 12 (2) ◽  
pp. 1196
Author(s):  
Widi Aribowo ◽  
Bambang Suprianto ◽  
Joko Joko
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Dc Motor ◽  
Motor Speed ◽  
Parameter Uncertainties ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Sine Cosine Algorithm ◽  
Better Than ◽  
Tree Seed

A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.

scholarly journals Prediction of Oxidation Stability of Biodiesel Derived from Waste and Refined Vegetable Oils by Statistical Approaches

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 407
Author(s):  
Hüseyin Çamur ◽  
Ahmed Muayad Rashid Al-Ani
Keyword(s):  
Neural Network ◽  
Fatty Acid ◽  
Mean Squared Error ◽  
Oxidation Stability ◽  
Engine Performance ◽  
Absolute Error ◽  
Feed Forward Neural Network ◽  
Elman Neural Network ◽  
Feed Forward ◽  
Long Chain

The oxidation stability (OX) of the biodiesel is an essential parameter mainly during storage, which reduces the quality of the biodiesel, thus affecting the engine performance. Moreover, many factors affect oxidation stability. Therefore, determining the most significant parameter is essential for achieving accurate predictions. In this paper, an empirical equation (Poisson Regression Model (PRM)), machine learning models (Multilayer Feed-Forward Neural Network (MFFNN), Cascade Feed-forward Neural Network (CFNN), Radial Basis Neural Network (RBFNN), and Elman neural network (ENN)) with various combinations of input parameters are utilized and employed to identify the most relevant parameters for prediction of the oxidation stability of biodiesel. This study measured the physicochemical properties of 39 samples of waste frying methyl ester and their blends with various percentages of palm biodiesel and refined canola biodiesel. To this aim, 14 parameters including concentration amount of WFME (X1), PME (X2), and RCME (X3) in the mixture, kinematic viscosity (KV) at 40 °C, density at 15 °C (D), cloud point (CP), pour point (PP), the estimation value of the sum of the saturated (∑SFAMs), monounsaturated (∑MUFAMs), polyunsaturated (∑PUFAMs), degree of unsaturation (DU), long-chain saturated factor (LCSF), very-long-chain fatty acid (VLCFA), and ratio (∑MUFAMs+∑PUFAMs∑SFAMs) fatty acid composition were considered. The results demonstrated that the RBFNN model with the combination of X1, X2, X3, ∑SFAMs, ∑MUFAMs, ∑PUFAMs. VLCFA, DU, LCSF, ∑MUFAMs+∑PUFAMs∑SFAMs, KV, and D has the lowest value of root mean squared error and mean absolute error. In the end, the results demonstrated that the RBFNN model performed well and presented high accuracy in estimating the value of OX for the biodiesel samples compared to PRM, MFFNN, CFNN, and ENN.

The Neural Network Control Algorithm Research of Straight Pull Single Crystal Furnace

2013 ◽  
Vol 756-759 ◽  
pp. 271-274
Author(s):  
Ming Yu Li ◽  
Hua Meng ◽  
Jie Zhao ◽  
Zeng Tao Xue ◽  
Ying Zhang
Keyword(s):  
Neural Network ◽  
Single Crystal ◽  
Control Method ◽  
Dc Motor ◽  
Network Control ◽  
Motor Speed ◽  
Control Effect ◽  
The Neural Network ◽  
Speed Change ◽  
Pid Neural Network

In straight pull single crystal furnaces pulling system, Traditional PID controllers parameter cant meet the changes of the DC motor system. In view of this defect that the traditional PID controller parameter can not turn in a timely manner with the DC motor speed change. This paper used a new control method PID neural network to the monocrystalline pulling speed control system and established PID neural network pulling system, at any time to adjust to the optimal PID parameters, to achieve an adaptive control effect and improve the quality of monocrystalline silicon.

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