Modified Krill Herd Swarm Optimization (MKHSO) Based Optimal Neural Network Model for Analysis Multi-Performance Parameters in Manufacturing System

2018 ◽  
Vol 17 (02) ◽  
pp. 197-212
Author(s):  
R. Prasanna Lakshmi ◽  
P. Nelson Raja
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Preventive Maintenance ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Mean Square ◽  
Operational Cost ◽  
Performance Parameters ◽  
Swarm Optimization ◽  
Krill Herd

Develop a multi-target exhibit by considering the workstation reliability for preventive maintenance perspective, the general availability of the framework for production purposes, and total operational expenses for both preventive support and production arranging decisions. Despite that, the greater parts of the reviews in upkeep optimization do not consider the creation necessities experienced eventually. In this paper, hybrid inspired optimization model for the performance analysis in the manufacturing industry is utilized. This forecast investigation neural Network considered for weight streamlining procedure alongside parameters, for example, Total Operational Cost (TOC), availability and reliability of assembling framework. Weight examination krill and swarm intelligence are used to limit Mean Square Error (MSE) for all parameters. All the perfect outcomes show the way that the refined slip-up qualities between the output of the trial values and the foreseen qualities are solidly proportionate to zero in the arranged framework. From the results, the proposed Modified Krill herd Swarm Optimization (MKHSO) based perfect neural framework exhibits a precision of 98.23%, which diverges from the existing methodology.

scholarly journals Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 351 ◽  
Author(s):  
Promphak Dawan ◽  
Kobsak Sriprapha ◽  
Songkiate Kittisontirak ◽  
Terapong Boonraksa ◽  
Nitikorn Junhuathon ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Output ◽  
Fuzzy Inference ◽  
Photovoltaic System ◽  
Mean Square ◽  
Fuzzy Inference Systems ◽  
Swarm Optimization ◽  
Pv Systems ◽  
Neuro Fuzzy ◽  
Inference Systems

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

scholarly journals O OPTIMALISASI NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK MEMPREDIKSI LAMA PENYINARAN MATAHARI DALAM MEMENUHI KEBUTUHAN ENERGI

2019 ◽  
Vol 4 (2) ◽  
pp. 52-62
Author(s):  
Wahyudin Hasyim ◽  
Alter Lasarudin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Particle Swarm ◽  
Mean Square ◽  
Swarm Optimization

Tingginya beban listrik  yang mencapai 325 MegaWatt, hal ini merupakan perhatian penting bagi pemerintah Provinsi Gorontalo dalam  kebutuhan energi listrik, maka perlu memprediksi lama penyinaran matarahari pada suatu daerah, Energi sel surya salah satunya bergantung pada lamanya penyinaran cahaya matahari. Diantaranya dengan melakukan perancangan model prediksi. Metode prediksi yang mimiliki nilai error terkecil adalah Neural Network, akan tetapi masih adanya kelemahan pada waktu pelatihan untuk mencapai konvergen dan overfitting. Maka  perlu dilakukan optimalisasi pada bobot jaringan dengan menggunakan Particle Swarm Optimazition, yang merupakan salah satu metode terbaik dalam optimasi. Dengan penggunaan optimasi yang diukur melalui hasil peroleha Root Mean Square Error (RMSE). Hasil pengujian terhadap algoritma menunjukkan bahwa nilai RMSE mengunakan Neural Network 0,131, sedangkan dengan penerapan optimasi dengan particle swarm optimization  hasil RMSE  0,127. Dengan penerapan metode optimasi terserbut dapat mengurangi nilai error

scholarly journals Perbandingan Metode Extreme Learning Machine dan Particle Swarm Optimization Extreme Learning Machine untuk Peramalan Jumlah Penjualan Barang

2016 ◽  
Vol 15 (1) ◽  
pp. 84
Author(s):  
Susila Handika ◽  
IAD Gririantari ◽  
Agus Dharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Mean Square ◽  
Swarm Optimization ◽  
Learning Machine ◽  
Range Error ◽  
Hidden Nodes

Extreme Learning Machine (ELM) merupakan salah satu metode pembelajaran dari Artificial Neural Network yang memberikan tingkat akurasi dan kecepatan yang lebih baik dari pada metode pembelajaran lainnya. Salah satu kelemahan dari metode ELM adalah jumlah hidden nodes ditentukan dengan cara try and error, sehingga tidak bisa diketahui berapa jumlah hidden nodes yang tepat untuk mendapatkan hasil peramalan menggunakan metode ELM. Untuk mengatasi masalah tersebut digunakan metode optimasi Particle Swarm Optimization untuk mencari jumlah hidden nodes yang optimal. Data yang digunakan untuk keperluan analisis adalah data time series penjualan barang salah satu minimarket di Bali. Hasil peramalan akan diukur mengunggunakan Mean Square Error (MSE) dengan data uji yang sama. Hasil penelitian menunjukkan metode PSO dapat diterapkan pada metode ELM untuk mengoptimasi jumlah hidden nodes. MSE yang dihasilkan oleh metode PSO ELM lebih kecil dibanding metode ELM. Selain itu range error yang dihasilkan oleh metode PSO ELM juga lebih kecil dibanding metode ELM DOI: 10.24843/MITE.1501.15

scholarly journals Aeroengine Fault Diagnosis Using Optimized Elman Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Jun Pi ◽  
Jiangbo Huang ◽  
Long Ma
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Small Samples ◽  
Support Vector ◽  
Mean Square ◽  
Diagnostic Ability ◽  
Elman Neural Network ◽  
Swarm Optimization ◽  
Training Samples

A new Elman Neural Network (ENN) optimized by quantum-behaved adaptive particle swarm optimization (QAPSO) is introduced in this paper. According to the root mean square error, QAPSO is used to select the best weights and thresholds of the ENN in training samples. The optimized neural network is applied to aeroengine fault diagnosis and is compared with other optimized ENN, original ENN, BP, and Support Vector Machine (SVM) methods. The results show that the QAPSO-ENN is more accurate and reliable in the aeroengine fault diagnosis than the conventional neural network and other ENN methods; QAPSO-ENN has great diagnostic ability in small samples.

scholarly journals Optimasi Bobot Jaringan Syaraf Tiruan Mengunakan Particle Swarm Optimization

2014 ◽  
Vol 8 (1) ◽  
pp. 25
Author(s):  
Harry Ganda Nugraha ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Mean Square Error ◽  
Particle Swarm ◽  
Mean Square ◽  
Swarm Optimization ◽  
Relative Variance ◽  
Artificial Neural

AbstrakMasalah peramalan adalah masalah yang sering ditemukan dalam proses pengambilan keputusan. Tool yang cukup populer untuk menangani masalah peramalan adalah jaringan syaraf tiruan. Jaringan syaraf tiruan banyak digunakan karena kemampuannya untuk meramalkan data nonlinear time series. Algoritma pembelajaran yang sering digunakan untuk memperbaiki bobot pada jaringan syaraf tiruan adalah backpropagation. Namun proses pembelajaran backpropagation terkadang menemui kendala seperti over fiting sehingga tidak dapat menggeneralisasi masalah. Untuk mengatasi masalah tersebut diusulkan penggunaan particle swarm optimization untuk melatih bobot pada jaringan. Performa dari masing-masing model akan diukur dengan mean square error, mean absolute percentage error, normalized mean square error, prediction of change in direction, average relative variance. Untuk keperluan analisis model digunakan data time series inflasi di indonesia. Metode yang diusulkan menunjukan sistem jaringan hybrid mampu menangani masalah peramalan data time series dengan performa mendekati jaringan syaraf tiruan backpropagation.. Kata kunci—jaringan syaraf tiruan, particle swarm optimization, prediction of change in direction, average relative variance .  AbstractForecasting problem is common problem that easily found in decision making process. The popular tool to handle that problem is artificial neural network. Artificial neural network have been widely use because its ability to forecast nonlinear time series data. The learning method that have been widely use to train artificial neural network weight is backpropagation. Otherwise backpropagation learning process sometimes find problem such as over fiting so it can’t generalized the problem. Particle swarm optimization method had been proposed to train artificial neural network weigth. Mean square error, mean absolute percentage error, normalized mean square error, prediction of change in direction, average relative variance had been use to measures the model performance. Indonesia inflation time series data had been use to analyzed the model. The proposed method show that hybrid system could handle the time series forecasting problem as good as backpropagation artificial neural network Keywords—artificial neural network, particle swarm optimization, prediction of change in direction, average relative variance.

Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

2019 ◽  
Author(s):  
Nguyen Cao Thang ◽  
Luu Xuan Hung
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Nonlinear Oscillators ◽  
Degree Of Freedom ◽  
Equivalent Linearization ◽  
Mean Square ◽  
Error Criterion ◽  
Stochastic Linearization ◽  
Local Mean ◽  
Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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