scholarly journals Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Xiangsheng Zhang ◽  
Feng Pan
Keyword(s):  
Parameters Optimization ◽  
Support Vector ◽  
Local Minima ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Glutamate Fermentation ◽  
Fermentation Model ◽  
Searching Ability ◽  
Ipso Algorithm ◽  
Strong Ability

Aimed at the parameters optimization in support vector machine (SVM) for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO) algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effectiveness of the proposed algorithm.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

Nonlinear Inertia Weigh Particle Swarm Optimization Combines Simulated Annealing Algorithm and Application in Function and SVM Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3467-3471 ◽  
Author(s):  
Bin Jiao ◽  
Zhi Xiang Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Local Equilibrium ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Performance ◽  
Support Vector ◽  
Swarm Optimization ◽  
Searching Ability

This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear decreasing algorithm to balance, then PSO combines with simulated annealing (SA). Finally, the optimization test experiments are carried out for the typical functions with the algorithm (ULWPSO-SA), and compare with the basic PSO algorithm. Simulation experiments show that local search ability of algorithm, convergence speed, stability and accuracy have been significantly improved. In addition, the novel algorithm is used in the parameter optimization of support vector machines (ULWPSOSA-SVM), and the experimental results indicate that it gets a better classification performance compared with SVM and PSO-SVM.

Emotion Fusion Recognition for Intelligent Surveillance with PSO-CSVM

2011 ◽  
Vol 225-226 ◽  
pp. 51-56
Author(s):  
Rui Hu Wang ◽  
Bin Fang
Keyword(s):  
Facial Expression ◽  
Speaker Recognition ◽  
Iris Recognition ◽  
Particle Swarm Optimization Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Intelligent Surveillance ◽  
Sensor Signals

The next generation of intelligent surveillance system should be able to recognize human’s spontaneous emotion state automatically. Compared to speaker recognition, sensor signals analyzing, fingerprint or iris recognition, etc, facial expression and body gesture processing are two mainly non-intrusive vision modalities, which provides potential action information for video surveillance. In our work, we care one kind of facial expression, i.e. anxiety and gesture motion only. Firstly facial expression and body gesture feature are extracted. Particle Swarm Optimization algorithm is used to select feature subset and parameters optimization. The selected features are trained or tested for cascaded Support Vector Machine to obtain a high-accuracy classifier.

A Novel Intelligent Modeling Method for Wood Drying Process

2011 ◽  
Vol 121-126 ◽  
pp. 647-651
Author(s):  
Dong Yan Zhang ◽  
Chun Yan Zhang ◽  
Liang Kuan Zhu ◽  
Zhi Duo Diao
Keyword(s):  
Swarm Intelligence ◽  
Parameters Optimization ◽  
Learning Ability ◽  
Support Vector ◽  
Drying Process ◽  
Wood Drying ◽  
Swarm Optimization ◽  
Nonlinear Characteristics ◽  
Intelligent Modeling ◽  
Swarm Intelligence Optimization

This paper investigates the development and intelligent modeling problem for a wood drying kiln process via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithms of Particle Swarm Optimization (PSO)-SVM and Genetic Algorithm (GA)-SVM were proposed for wood drying process with strong coupling and nonlinear characteristics. The simulation results showed that both of these two kinds of swarm intelligence optimization algorithm could get the appropriate parameters of SVM effectively, and by contrast, PSO showed a better learning ability and generalization in wood drying process modeling, and could establish predictive model with better accessibility.

scholarly journals PID Parameters Optimization Research for Hydro Turbine Governor by an Improved Fuzzy Particle Swarm Optimization Algorithm

2016 ◽  
Vol 10 (1) ◽  
pp. 101-117 ◽  
Author(s):  
Chen Gonggui ◽  
Du Yangwei ◽  
Guo Yanyan ◽  
Huang Shanwai ◽  
Liu Lilan
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Control ◽  
Parameter Optimization ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Parameters Optimization ◽  
Simulation Experiments ◽  
Swarm Optimization

Parameter optimization of water turbine regulating system (WTRS) is decisive in providing support for the power quality and stability analysis of power system. In this paper, an improved fuzzy particle swarm optimization (IFPSO) algorithm is proposed and used to solve the optimization problem for WTRS under frequency and load disturbances conditions. The novel algorithm which is based on the standard particle swarm optimization (PSO) algorithm can speed up the convergence speed and improve convergence precision with combination of the fuzzy control thought and the crossover thought in genetic algorithm (GA). The fuzzy control is employed to get better dynamics of balance between global and local search capabilities, and the crossover operator is introduced to enhance the diversity of particles. Two different types of WTRS systems are built and analyzed in the simulation experiments. Furthermore, the sum of regulating time and another number that is the integral of sum for absolute value of system error and the squared governor output signal is considered as the fitness function of this algorithm. The simulation experiments for parameter optimization problem of WTRS system are carried out to confirm the validity and superiority of the proposed IFPSO, as compared to standard PSO, Ziegler Nichols (ZN) algorithm and fuzzy PID algorithm in terms of parameter optimization accuracy and convergence speed. The simulation results reveal that IFPSO significantly improves the dynamic performance of system under all of the running conditions.

Sign in / Sign up

Export Citation Format

Share Document