Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.

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Position Control of Ultrasonic Motor Using IMC-PID Combined with Tribes Type NN Algorithm

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.2-3.12 ◽

2011 ◽

Vol 2-3 ◽

pp. 12-17

Author(s):

Sheng Lin Mu ◽

Kanya Tanaka

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Pid Control ◽

Position Control ◽

Back Propagation ◽

Particle Swarm ◽

Pso Algorithm ◽

Ultrasonic Motor ◽

Swarm Optimization

In this paper, we propose a novel scheme of IMC-PID control combined with a tribes type neural network (NN) for the position control of ultrasonic motor (USM). In this method, the NN controller is employed for tuning the parameter in IMC-PID control. The weights of NN are designed to be updated by the tribes-particle swarm optimization (PSO) algorithm. This method makes it possible to compensate for the characteristic changes and nonlinearity of USM. The parameter-free tribes-PSO requires no information about the USM beforehand; hence its application overcomes the problem of Jacobian estimation in the conventional back propagation (BP) method of NN. The effectiveness of the proposed method is confirmed by experiments.

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Training Back-Propagation Neural Network for Target Localization Using Improved Particle Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.1384 ◽

2013 ◽

Vol 333-335 ◽

pp. 1384-1387

Author(s):

Jin Jie Yao ◽

Xiang Ju ◽

Li Ming Wang ◽

Jin Xiao Pan ◽

Yan Han

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Back Propagation ◽

Particle Swarm ◽

Back Propagation Neural Network ◽

Position Estimation ◽

Target Localization ◽

Prediction Ability ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization

Target localization technology has been intensively studied and broadly applied in many fields. This paper presents one improved particle swarm optimization technique in training a back-propagation neural network for position estimation in target localization. The proposed scheme combines particle swarm optimization (PSO), back-propagation neural network (BP), adaptive inertia weight and hybrid mutation, called IPSO-BP. To verify the proposed IPSO-BP approach, comparisons between the PSO-based BP approach (PSO-BP) and general back-propagation neural network (BP) are made. The computational results show that the proposed IPSO-BP approach exhibits much better performance in the training process and better prediction ability in the validation process than those using the other two base line approaches.

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An Intelligent System Based on Back Propagation Neural Network and Particle Swarm Optimization for Detection of Prostate Cancer from Benign Hyperplasia of Prostate

Journal of Health & Medical Informatics ◽

10.4172/2157-7420.1000158 ◽

2014 ◽

Vol 05 (03) ◽

Author(s):

Mustafa Ghaderzadeh

Keyword(s):

Neural Network ◽

Prostate Cancer ◽

Particle Swarm Optimization ◽

Intelligent System ◽

Back Propagation ◽

Particle Swarm ◽

Back Propagation Neural Network ◽

Swarm Optimization ◽

Benign Hyperplasia

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Grade estimation using a hybrid method of back-propagation artificial neural network and particle swarm optimization with integrated samples coordinate and local variability

Computers & Geosciences ◽

10.1016/j.cageo.2021.104981 ◽

2021 ◽

pp. 104981

Author(s):

Saeed Soltani-Mohamadi ◽

Fateme Sadat Hoseinian ◽

Malieheh Abbaszadeh ◽

Mahdi Khodadadzadeh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

Hybrid Method ◽

Back Propagation ◽

Particle Swarm ◽

Swarm Optimization ◽

Grade Estimation ◽

Artificial Neural ◽

Local Variability

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A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization

Complexity ◽

10.1155/2020/3067024 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

Ghusn Abdul Redha Ibraheem ◽

Ahmad Taher Azar ◽

Ibraheem Kasim Ibraheem ◽

Amjad J. Humaidi

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Fractional Derivative ◽

Particle Swarm ◽

Fruit Fly ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Fruit Fly Optimization ◽

Hidden Layer ◽

Fractional Control

The design of a swarm optimization-based fractional control for engineering application is an active research topic in the optimization analysis. This work offers the analysis, design, and simulation of a new neural network- (NN) based nonlinear fractional control structure. With suitable arrangements of the hidden layer neurons using nonlinear and linear activation functions in the hidden and output layers, respectively, and with appropriate connection weights between different hidden layer neurons, a new class of nonlinear neural fractional-order proportional integral derivative (NNFOPID) controller is proposed and designed. It is obtained by approximating the fractional derivative and integral actions of the FOPID controller and applied to the motion control of nonholonomic differential drive mobile robot (DDMR). The proposed NNFOPID controller’s parameters consist of derivative, integral, and proportional gains in addition to fractional integral and fractional derivative orders. The tuning of these parameters makes the design of such a controller much more difficult than the classical PID one. To tackle this problem, a new swarm optimization algorithm, namely, MAPSO-EFFO algorithm, has been proposed by hybridization of the modified adaptive particle swarm optimization (MAPSO) and the enhanced fruit fly optimization (EFFO) to tune the parameters of the NNFOPID controller. Firstly, we developed a modified adaptive particle swarm optimization (MAPSO) algorithm by adding an initial run phase with a massive number of particles. Secondly, the conventional fruit fly optimization (FFO) algorithm has been modified by increasing the randomness in the initialization values of the algorithm to cover wider searching space and then implementing a variable searching radius during the update phase by starting with a large radius which decreases gradually during the searching phase. The tuning of the parameters of the proposed NNFOPID controller is carried out by reducing the MS error of 0.000059, whereas the MSE of the nonlinear neural system (NNPID) is equivalent to 0.00079. The NNFOPID controller also decreased control signals that drive DDMR motors by approximately 45 percent compared to NNPID and thus reduced energy consumption in circular trajectories. The numerical simulations revealed the excellent performance of the designed NNFOPID controller by comparing its performance with that of nonlinear neural (NNPID) controllers on the trajectory tracking of the DDMR with different trajectories as study cases.

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