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 A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

Minerals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 159
Author(s):  
Nan Lin ◽  
Yongliang Chen ◽  
Haiqi Liu ◽  
Hanlin Liu
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Geochemical Data ◽  
Support Vector ◽  
Learning Models ◽  
Hyperparameter Optimization ◽  
Mineral Prospectivity ◽  
Swarm Intelligence Optimization ◽  
Machine Learning Models

Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.

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.

scholarly journals A Swarm Intelligence Based Weighted Feature Extraction and Classification using SVM for Sentimental Exploration

2019 ◽  
Vol 8 (3) ◽  
pp. 883-890
Keyword(s):  
Swarm Intelligence ◽  
Fitness Function ◽  
Performance Measure ◽  
Support Vector ◽  
Relevant Feature ◽  
Data Set ◽  
Swarm Optimization ◽  
Recall Accuracy ◽  
Classification Technique ◽  
F Measure

The goal of Sentiment Exploration (SE) is used for mining the accurate sentiments which are very beneficial for businesses, governments, and individuals, the opinions, recommendations, ratings, and feedbacks are becoming an important aspect in present scenarios. The proposed methodology likewise attempts to introduce a swarm intelligence based sentimental supervised methodology. In order to obtain a relevant feature data set from a large number of data samples, this method used particle swarm optimization to attain the utmost optimum feature set. The evaluation of the optimum feature set is obtained by means of using Minimum Redundancy and Maximum Relevancy measure as the fitness function. The categorization of the extracted feature set is accomplished with the Support Vector Machine classification technique. The experimental outcome for the suggested method is evaluated using four performance measure like precision, recall, accuracy, and f-measure and showed that proposed swarm intelligent based classification method has better performance using IMDB, Movie Lens and Trip Advisor Data Samples.

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.

The Swarm Intelligence Optimization Algorithm and its Application

2013 ◽  
Vol 711 ◽  
pp. 659-664
Author(s):  
Li Shan Li
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Shuffled Frog Leaping ◽  
Swarm Intelligence Optimization

In the article, three kinds of swarm intelligence optimization algorithm are discussed including the ant colony optimization (ACO) algorithm, the particle swarm optimization (PSO) algorithm and the shuffled frog leaping algorithm (SFLA). The principle, development and application of each algorithm is introduced. Finally, an example of TSP is used to test the performance of ACO.

scholarly journals Distributed Systematic Grid-Connected Inverter Using IGBT Junction Temperature Predictive Control Method: An Optimization Approach

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 825
Author(s):  
Zhengping Wang ◽  
Guoyi Li ◽  
Ming-Lang Tseng ◽  
Wai-Peng Wong ◽  
Boying Liu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Learning Ability ◽  
Junction Temperature ◽  
Support Vector ◽  
Machine Model ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Grid Connected Inverter ◽  
Chicken Swarm Optimization

Distributed systematic grid-connected inverter practice needs to improve insulated gate bipolar transistor (IGBT) stability to ensure the safe operation. This study is to ensure the safety and reliability operation of the IGBT module in symmetry to meet the reliable and stable distributed systematic grid-connected inverter practice and the junction temperature is a parameter to assess its operating state. It is difficult to accurately acquire the IGBT junction temperature to be solved by a single method of combining the test and the modeling. The saturation voltage drop or collector current and module junction temperature data under different power cycles are measured by the power cycle test and the single pulse test. The improved chicken swarm optimization increases the chickens diversity and self-learning ability. The prediction model of the improved chicken swarm optimization-support vector machine is proposed to forecast the module junction temperature. The result showed to compare with the particle swarm optimization-support vector machine model and chicken swarm optimization-support vector machine model and showed the coincidence degree between the proposed model prediction value and the true value is higher. The mean absolute error ratio indicates the proposed model has a smaller error and a better prediction performance. The proposed model has a positive impact on improving the distributed systematic grid-connected inverter industrial development and promotes the new energy usage.

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.

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