An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement

2017 ◽  
Vol 10 (3) ◽  
pp. 841-850 ◽  
Author(s):  
Bin Qian ◽  
Qian-Qian Wang ◽  
Rong Hu ◽  
Zhi-Jie Zhou ◽  
Chuan-Qiang Yu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Rule Base ◽  
Computing Technology ◽  
Permeability Measurement ◽  
Swarm Optimization ◽  
Belief Rule Base

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

2019 ◽  
Vol 29 (2) ◽  
pp. 711-721 ◽  
Author(s):  
Xiliang Zhang ◽  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Quang-Hieu Tran ◽  
Dinh-An Nguyen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Ground Vibration ◽  
Open Pit ◽  
Swarm Optimization ◽  
Open Pit Mines ◽  
Computing Model

Subtractive Clustering and Particle Swarm Optimization Based Fuzzy Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 108-122 ◽  
Author(s):  
Halima Salah ◽  
Mohamed Nemissi ◽  
Hamid Seridi ◽  
Herman Akdag
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Particle Swarm ◽  
Main Idea ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Subtractive Clustering ◽  
Data Sets ◽  
Fuzzy Classifier ◽  
Swarm Optimization

Setting a compact and accurate rule base constitutes the principal objective in designing fuzzy rule-based classifiers. In this regard, the authors propose a designing scheme based on the combination of the subtractive clustering (SC) and the particle swarm optimization (PSO). The main idea relies on the application of the SC on each class separately and with a different radius in order to generate regions that are more accurate, and to represent each region by a fuzzy rule. However, the number of rules is then affected by the radiuses, which are the main preset parameters of the SC. The PSO is therefore used to define the optimal radiuses. To get good compromise accuracy-compactness, the authors propose using a multi-objective function for the PSO. The performances of the proposed method are tested on well-known data sets and compared with several state-of-the-art methods.

scholarly journals Performance Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for solving the Travelling Salesman Problem

2019 ◽  
Vol 8 (2S4) ◽  
pp. 91-95
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Soft Computing ◽  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Ant Colony ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Soft Computing Techniques

The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.

Fuzzy-Based Gas Turbine Engine Fuel Controller Design Using Particle Swarm Optimization

2011 ◽  
Vol 110-116 ◽  
pp. 3215-3222 ◽  
Author(s):  
M. Montazeri-Gh ◽  
E. Mohammadi ◽  
S. Jafari
Keyword(s):  
Particle Swarm Optimization ◽  
Gas Turbine ◽  
Fuzzy Logic Controller ◽  
Particle Swarm ◽  
Gas Turbine Engine ◽  
Time Response ◽  
Rule Base ◽  
Engine Fuel ◽  
Swarm Optimization ◽  
Turbine Engine

This paper presents the application of Particle Swarm Optimization (PSO) algorithm for optimization of the Gas Turbine Engine (GTE) fuel control system. In this study, the Wiener model for GTE as a block structure model is firstly developed. This representation is an appropriate model for controller tuning. Subsequently, based on the nonlinear GTE nature, a Fuzzy Logic Controller (FLC) with an initial rule base is designed for the engine fuel system. Then, the initial FLC is tuned by PSO with emphasis on the engine safety and time response. In this study, the optimization process is performed in two stages during which the Data Base (DB) and the Rule Base (RB) of the initial FLC are tuned sequentially. The results obtained from the simulation show the ability of the approach to achieve an acceptable time response and to attain a safe operation by limiting the turbine rotor acceleration.

A Neuro-Fuzzy Expert System Trained by Particle Swarm Optimization for Stock Price Prediction

2012 ◽  
pp. 633-650
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Milad Avazbeigi ◽  
Meysam Alizadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Stock Market ◽  
Soft Computing ◽  
Stock Price ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Neuro Fuzzy ◽  
Input Variables

In today’s competitive markets, prediction of financial variables has become a critical issue. Especially in stock market analysis where a wrong prediction may result in a big loss in terms of time and money, having a robust prediction is a crucial issue. To model the chaotic, noisy, and evolving behavior of stock market data, new powerful methods should be developed. Soft Computing methods have shown a great confidence in such environments where there are many uncertain factors. Also it has been observed through many experiments that the hybridization of different soft computing techniques such as fuzzy logic, neural networks, and meta-heuristics usually results in better results than simply using one method. This chapter presents an adaptive neuro-fuzzy inference system (ANFIS), trained by the particle swarm optimization (PSO) algorithm for stock price prediction. Instead of previous works that have emphasized on gradient base or least square (LS) methods for training the neural network, four different strategies of PSO are implemented: gbest, lbest-a, lbest-b, and Euclidean. In the proposed fuzzy rule based system some technical and fundamental indexes are applied as input variables. In order to generate membership functions (MFs), a robust noise rejection clustering algorithm is developed. The proposed neuro-fuzzy model is applied for an automotive part-making manufactory in an Asia stock market. The results show the superiority of the proposed model in comparison with the available models in terms of error minimization, robustness, and flexibility.

Particle Swarm Optimization Based Technique for Deposition Parameters Optimization of Magnetron Sputtering Process

2015 ◽  
Vol 1109 ◽  
pp. 486-490
Author(s):  
Norlina Mohd Sabri ◽  
Nor Diyana Md Sin ◽  
Siti Shafura Ash Karim ◽  
Mazidah Puteh ◽  
Mohamad Rusop
Keyword(s):  
Thin Film ◽  
Particle Swarm Optimization ◽  
Magnetron Sputtering ◽  
Soft Computing ◽  
Desirability Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Zno Thin Film ◽  
Swarm Optimization ◽  
Deposition Parameters

This study presents a soft computing based technique in the deposition parameters optimization of RF Magnetron Sputtering process. Particle Swarm Optimization (PSO) has been chosen due to its good performance in solving various optimization problems. The material used in this study was zinc oxide (ZnO) and there were four deposition parameters involved the optimization process. The deposition parameters were RF power, deposition time, oxygen flow rate and substrate temperature. The aim of the study was to obtain the optimal combination for the selected deposition parameters in order to produce the desirable ZnO thin film properties. In this study, the Desirability Function had been adapted as the fitness function for PSO. Desirability function is one of the commonly used statistical method for obtaining optimal process parameter design. The result from the PSO based optimization technique was then compared with actual laboratory result. Based on the observation made, the PSO based technique has been proven to be reliable and satisfactory in obtaining the optimal deposition parameters of ZnO thin film. It is expected that this soft computing based technique for optimizing the deposition parameters could reduce the trial and error method before the experiment is conducted in the fabrication process.

Adaptive Neuro-Fuzzy Control Approach Based on Particle Swarm Optimization

2010 ◽  
Vol 1 (4) ◽  
pp. 1-16
Author(s):  
Gomaa Zaki El-Far
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Rule Base ◽  
Swarm Optimization ◽  
Linear Dynamical Systems ◽  
Neuro Fuzzy ◽  
Non Linear

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold to control the velocity of the particles, avoids clustering of the particles, and maintains the diversity of the population in the search space. The mechanism of MPSO has better potential to explore good solutions in new search spaces. The proposed MPSO algorithm is also used to tune and optimize the controller parameters like the scaling factors, the membership functions, and the rule base. To illustrate the adaptation process, the proposed neuro-fuzzy controller based on MPSO algorithm is applied successfully to control the behavior of both non-linear single machine power systems and non-linear inverted pendulum systems. Simulation results demonstrate that the adaptive neuro-fuzzy logic controller application based on MPSO can effectively and robustly enhance the damping of oscillations.

Sign in / Sign up

Export Citation Format

Share Document