Comparing the performance of neural networks developed by using Levenberg–Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process

2012 ◽  
Vol 39 (3) ◽  
pp. 2397-2407 ◽  
Author(s):  
Indrajit Mukherjee ◽  
Srikanta Routroy
Keyword(s):  
Neural Networks ◽  
Gradient Descent ◽  
Grinding Process ◽  
Multiple Response ◽  
Descent Algorithm ◽  
Gradient Descent Algorithm ◽  
Levenberg Marquardt ◽  
Quasi Newton

scholarly journals A Novel Technique for Solving Fully Fuzzy Nonlinear Systems Based on Neural Networks

2020 ◽  
Vol 07 (01) ◽  
pp. 93-107 ◽  
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov
Keyword(s):  
Neural Networks ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Complex Systems ◽  
Gradient Descent ◽  
Descent Algorithm ◽  
Novel Technique ◽  
New Approaches ◽  
Gradient Descent Algorithm

Predicting the solutions of complex systems is a crucial challenge. Complexity exists because of the uncertainty as well as nonlinearity. The nonlinearity in complex systems makes uncertainty irreducible in several cases. In this paper, two new approaches based on neural networks are proposed in order to find the estimated solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the estimated solutions, a gradient descent algorithm is proposed in order to train the proposed networks. An example is proposed in order to show the efficiency of the considered approaches.

Control of Multivariable Coupling System Based on Improved PID Neural Networks

2012 ◽  
Vol 157-158 ◽  
pp. 386-389
Author(s):  
Jun Kang ◽  
Wen Jun Meng
Keyword(s):  
Neural Networks ◽  
High Precision ◽  
Gradient Descent ◽  
Pso Algorithm ◽  
Initial Structure ◽  
Descent Algorithm ◽  
Coupling System ◽  
Gradient Descent Algorithm ◽  
Short Time

The paper analyzes the characteristics and the situation of the control of multivariable coupling system, combining improved PID neural networks with PSO algorithm, and finally designs a suitable controller model. To achieve a controller model, an initial structure of PID neural networks is established in the first place. Then weights in the network is initialized by PSO algorithm and optimized by improved gradient descent algorithm. A simulation, MIMO coupling system, prove this controller has such characteristics with short time and high precision. The research of the paper provides a new idea and approach for control of complex coupling system.

scholarly journals An Improved Radial Basis Function Networks in Networks Weights Adjustment for Training Real-World Nonlinear Datasets

2019 ◽  
Vol 8 (1) ◽  
pp. 63
Author(s):  
Lim Eng Aik ◽  
Tan Wei Hong ◽  
Ahmad Kadri Junoh
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Real World ◽  
Basis Function ◽  
Gradient Descent ◽  
Radial Basis Function Networks ◽  
Training Algorithm ◽  
Descent Algorithm ◽  
Gradient Descent Algorithm ◽  
Radial Basis

In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks weight. The gradient descent algorithm is a widely used weight adjustment algorithm in most of neural networks training algorithm. However, the method is known for its weakness for easily trap in local minima. It suffers from a random weight generated for the networks during initial stage of training at input layer to hidden layer networks. The performance of radial basis function networks (RBFN) has been improved from different perspectives, including centroid initialization problem to weight correction stage over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the weight produces by the algorithm. To solve this problem, an improved gradient descent algorithm for finding initial weight and improve the overall networks weight is proposed. This improved version algorithm is incorporated into RBFN training algorithm for updating weight. Hence, this paper presented an improved RBFN in term of algorithm for improving the weight adjustment in RBFN during training process. The proposed training algorithm, which uses improved gradient descent algorithm for weight adjustment for training RBFN, obtained significant improvement in predictions compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment. The proposed improved network called IRBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to IRBFN for root mean square error (RMSE) values with standard RBFN. The IRBFN yielded a promising result with an average improvement percentage more than 40 percent in RMSE.

scholarly journals Prediction of Draft Force of a Chisel Cultivator Using Artificial Neural Networks and Its Comparison with Regression Model

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 451 ◽  
Author(s):  
Yousef Abbaspour-Gilandeh ◽  
Masoud Fazeli ◽  
Ali Roshanianfard ◽  
Mario Hernández-Hernández ◽  
Iván Gallardo-Bernal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Regression Model ◽  
Correlation Coefficient ◽  
Conjugate Gradient ◽  
Gradient Descent ◽  
Descent Algorithm ◽  
Scaled Conjugate Gradient ◽  
Gradient Descent Algorithm ◽  
Draft Force

In this study, artificial neural networks (ANNs) were used to predict the draft force of a rigid tine chisel cultivator. The factorial experiment based on the randomized complete block design (RCBD) was used to obtain the required data and to determine the factors affecting the draft force. The draft force of the chisel cultivator was measured using a three-point hitch dynamometer and data were collected using a DT800 datalogger. A recurrent back-propagation multilayer network was selected to predict the draft force of the cultivator. The gradient descent algorithm with momentum, Levenberg–Marquardt algorithm, and scaled conjugate gradient descent algorithm were used for network training. The tangent sigmoid transfer function was the activation functions in the layers. The draft force was predicted based on the tillage depth, soil moisture content, soil cone index, and forward speed. The results showed that the developed ANNs with two hidden layers (24 and 26 neurons in the first and second layers, respectively) with the use of the scaled conjugate gradient descent algorithm outperformed the networks developed with other algorithms. The average simulation accuracy and the correlation coefficient for the prediction of draft force of a chisel cultivator were 99.83% and 0.9445, respectively. The linear regression model had a much lower accuracy and correlation coefficient for predicting the draft force compared to the ANNs.

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