Improved Levenberg–Marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beams

Penelitian ini dilakukan untuk mengkomparasi Levenberg-Marquardt (Lm) Dengan Broyden, Fletcher, Goldfarb, And Shanno Quasi-Newton (Bfgs) pada Penerapan Backpropagation Neural Network (BPNN) untuk memprediksi data Kecepatan Angin rata-rata. Data yang digunakan pada penelitian ini adalah data kecepatan angin rata-rata harian pada bulan Januari 2010 sampai Desember 2014 di Banjarbaru, Kalimantan selatan . Kecepatan angin ditentukan oleh perbedaan tekanan udara antara tempat asal dan tujuan angin dan daerah yang dilaluinya, Prediksi salah satu teknik yang paling penting dalam mengetahui kecepatan angin yang dihasilkan. Hasil Komparasi analisis Levenberg-Marquardt (Lm) Dengan Broyden, Fletcher, Goldfarb, And Shanno Quasi-Newton (Bfgs) menunjukkan bahwa Pada penelitian ini hasil terbaik yang didapatkan menggunakan Levenberg Marquardt (trainlm) dengan akurasi sebesar 95,93%, setelah dibandingkan dengan performance maksimum dari algoritma BPNN yang lain. model BPNN merupakan metode terbaik yang digunakan untuk prediksi kecepatan angin rata-rata harian dengan nilai RMSE sebesar 1,3378.Kata kunci : Kecepatan Angin, Backpropagation Neural Network, Broyden, Fletcher, Goldfarb, And Shanno Quasi-Newton , Levenberg-Marquardt

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Hybrid backpropagation neural network-particle swarm optimization for seismic damage building prediction

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v14.i1.pp360-367 ◽

2019 ◽

Vol 14 (1) ◽

pp. 360

Author(s):

Marina Yusoff ◽

Faris Mohd Najib ◽

Rozaina Ismail

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Damage Index ◽

Particle Swarm ◽

Seismic Damage ◽

Backpropagation Neural Network ◽

Swarm Optimization ◽

Local Optima ◽

Damage Indices ◽

Prediction Problems

The evaluation of the vulnerability of buildings to earthquakes is of prime importance to ensure a good plan can be generated for the disaster preparedness to civilians. Most of the attempts are directed in calculating the damage index of buildings to determine and predict the vulnerability to certain scales of earthquakes. Most of the solutions used are traditional methods which are time consuming and complex. Some of initiatives have proven that the artificial neural network methods have the potential in solving earthquakes prediction problems. However, these methods have limitations in terms of suffering from local optima, premature convergence and overfitting. To overcome this challenging issue, this paper introduces a new solution to the prediction on the seismic damage index of buildings with the application of hybrid back propagation neural network and particle swarm optimization (BPNN-PSO) method. The prediction was based on damage indices of 35 buildings around Malaysia. The BPNN-PSO demonstrated a better result of 89% accuracy compared to the traditional backpropagation neural network with only 84%. The capability of PSO supports fast convergence method has shown good effort to improve the processing time and accuracy of the results.

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Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-03-2021-0073 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Zhigang Wang ◽

Aijun Li ◽

Lihao Wang ◽

Xiangchen Zhou ◽

Boning Wu

Keyword(s):

Neural Network ◽

Particle Swarm ◽

Particle Swarm Algorithm ◽

Aerodynamic Parameter ◽

Content Type ◽

Flight Data ◽

Output Error ◽

Aerodynamic Derivatives ◽

Levenberg Marquardt ◽

Error Method

Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

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