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1
Vetter, Yves-Alain. « Bacterial foraging with cell-free enzymes / ». Thesis, Connect to this title online ; UW restricted, 1998. http://hdl.handle.net/1773/11033.
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2
Ladevèze, Simon. « Functional and structural insights into Glycoside Hydrolase family 130 enzymes : implications in carbohydrate foraging by human gut bacteria ». Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0010/document.
Texte intégralRésumé :
Les relations entre bactéries intestinales, aliments et hôte jouent un rôle crucial dans lemaintien de la santé humaine. La caractérisation fonctionnelle d’Uhgb_MP, une enzyme dela famille 130 des glycoside hydrolases découverte par métagénomique fonctionnelle, arévélé une nouvelle fonction de dégradation par phosphorolyse des polysaccharides de laparoi végétale et des glycanes de l'hôte tapissant l'épithélium intestinal. Les déterminantsmoléculaires de la spécificité d’Uhgb_MP vis-à-vis des mannosides ont été identifiés grâce àla résolution de sa structure cristallographique, sous forme apo et en complexe avec sesligands. Un nouveau procédé de synthèse par phosphorolyse inverse d'oligosaccharidesmannosylés à haute valeur ajoutée, a aussi été développé. Enfin, la caractérisationfonctionnelle de la protéine BACOVA_03624 issue de Bacteroides ovatus ATCC 8483, unebactérie intestinale hautement prévalente, a révélé que la famille GH130 comprend à la foisdes glycoside-hydrolases et des glycoside-phosphorylases capables de dégrader lesmannosides et les galactosides, et de les synthétiser par phosphorolyse inverse et/outransglycosylation. L’ensemble de ces résultats, ainsi que l’identification d’inhibiteurs desenzymes de la famille GH130, ouvrent de nouvelles perspectives pour l'étude et le contrôledes interactions microbiote-hôteThe interplay between gut bacteria, food and host play a key role in human health. Thefunctional characterization of Uhgb_MP, an enzyme belonging to the family 130 of glycosidehydrolases, discovered by functional metagenomics, revealed novel functions of plant cellwall polysaccharide and host glycan degradation by phosphorolysis. The moleculardeterminants of Uhgb_MP specificity towards mannosides were identified by solving itscrystal structure, in apo form and in complex with its ligands. A new process of high addedvalue mannosylated oligosaccharide synthesis by reverse-phosphorolysis was alsodeveloped. Finally, the functional characterization of the BACOVA_03624 protein fromBacteroides ovatus ATCC 8483, a highly prevalent gut bacterium, revealed that GH130 familyboth contains glycoside phosphorylases and glycoside hydrolases, which are able to degrademannosides and galactosides, and to synthesize them by reverse-phosphorolysis and/ortransglycosylation. All these results, together with the identification of GH130 enzymeinhibitors, open new perspectives for studying, and potentially also for controlling,interactions between host and gut microbes
3
Harso, Wahyu [Verfasser], Eckhard [Gutachter] George, Christof [Gutachter] Engels et Klaus [Gutachter] Dittert. « The mycorrhizal plant root system : foraging activities and interaction with soil bacteria in heterogeneous soil environments / Wahyu Harso. Gutachter : Eckhard George ; Christof Engels ; Klaus Dittert ». Berlin : Lebenswissenschaftliche Fakultät, 2016. http://d-nb.info/1112193022/34.
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4
Tang, W. J. « Optimisation algorithms inspired from modelling of bacterial foraging patterns and their applications ». Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490623.
Texte intégralRésumé :
Research in biologically-inspired optimisation has been fl<;lurishing over the past decades. This approach adopts a bott0!ll-up viewpoint to understand and mimic certain features of a biological system. It has been proved useful in developing nondeterministic algorithms, such as Evolutionary Algorithms (EAs) and Swarm Intelligence (SI). Bacteria, as the simplest creature in nature, are of particular interest in recent studies. In the past thousands of millions of years, bacteria have exhibited a self-organising behaviour to cope with the natural selection. For example, bacteria have developed a number of strategies to search for food sources with a very efficient manner. This thesis explores the potential of understanding of a biological system by modelling the' underlying mechanisms of bacterial foraging patterns and investigates their applicability to engineering optimisation problems. :rvlodelling plays a significant role in understanding bacterial foraging behaviour. Mathematical expressions and experimental observations have been utilised to represent biological systems. However, difficulties arise from the lack of systematic analysis of the developed models and experimental data. Recently, Systems Biology has be,en proposed to overcome this barrier, with the effort from a number of research fields, including Computer Science and Systems Engineering. At the same time, Individual-based Modelling (IbM) has emerged to assist the modelling of a biological system. Starting from a basic model of foraging and proliferation of bacteria, the development of an IbM of bacterial systems of this thesis focuses on a Varying Environment BActerial Model (VEBAM). Simulation results demonstrate that VEBAM is able to provide a new perspective to describe interactions between the bacteria and their food environment. Knowledge transfer from modelling of bacterial systems to solving optimisation problems also composes an important part of this study. Three Bacteriainspired Algorithms (BalAs) have been developed to bridge the gap between modelling and optimisation. These algorithms make use of the. self-adaptability of individual bacteria in the group searching activities described in VEBAM, while incorporating a variety of additional features. In particular, the new bacterial foraging algorithm with varying population (BFAVP) takes bacterial metabolism into consideration. The group behaviour in Particle Swarm Optimiser (PSO) is adopted in Bacterial Swarming Algorithm (BSA) to enhance searching ability. To reduce computational time, another algorithm, a Paired-bacteria Optimiser (PBO) is designed specifically to further explore the capability of BalAs. Simulation studies undertaken against a wide range of benchmark functions demonstrate a satisfying performance with a reasonable convergence speed. To explore the potential of bacterial searching ability in optimisation undertaken in a varying environment, a dynamic bacterial foraging algorithm (DBFA) is developed with the aim of solving optimisation in a time-varying environment. In this case, the balance between its convergence and exploration abilities is investigated, and a new scheme of reproduction is developed which is different froin that used for static optimisation problems. The simulation studies have been undertaken and the results show that the DBFA can adapt to various environmental changes rapidly. One of the challenging large-scale complex optimisation problems is optimal power flow (OPF) computation. BFAVP shows its advantage in solving this problem. A simulation study has been performed on an IEEE 30-bus system, and the results are compared with PSO algorithm and Fast Evolutionary Programming (FEP) algorithm, respectively. Furthermore, the OPF problem is extended for consideration in varying environments, on which DBFA has been evaluated. A simulation study has been undertaken on both the IEEE 30-bus system and the IEEE l1S-bus system, in compariso~ with a number of existing algorithms. The dynamic OPF problem has been tackled for the first time in the area of power systems, and the results obtained are encouraging, with a significant amount of energy could possibly being saved. Another application of BaIA in this thesis is concerned with estimating optimal parameters of a power transformer winding model using BSA. Compared with Genetic Algorithm (GA), BSA is able to obtain a more satisfying result in modelling the transformer winding, which could not be achieved using a theoretical transfer function model.
5
Nasir, Ahmad. « Bacterial foraging and spiral dynamics based metaheuristic algorithms for global optimisation with engineering applications ». Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/7068/.
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6
Supriyono, Heru. « Novel bacterial foraging optimisation algorithms with application to modelling and control of flexible manipulator systems ». Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/2122/.
Texte intégralRésumé :
Biologically-inspired soft-computing algorithms, which were developed by mimicking evolution and foraging techniques of animals in nature, have attracted significant attention of researchers. The works are including the development of the algorithm itself, its modification and its application in broad areas. This thesis presents works on biologically-inspired algorithm based on bacterial foraging algorithm (BFA) and its performance evaluation in modelling and control of dynamic systems. The main aim of the research is to develop new modifications of BFA and its combination with other soft computing techniques and test their performances in modelling and control of dynamic systems. Modification of BFA focuses for improving its convergence in terms of speed and accuracy. The performances of modified BFAs are assessed in comparison to that of original BFA. In the original BFA, in this thesis referred as standard BFA (SBFA), bacteria use constant chemotactic step size to head to global optimum location. Very small chemotactic step size around global optimum location will assure bacteria find the global optimum point. However, a large number of steps is needed for the whole optimisation process. Moreover, there is potential for the algorithm to be trapped in one of the local optima. On the contrary, big chemotactic step size will assure bacteria have faster convergence speed but the literature shows that it results oscillation around global optimum point and the algorithm potentially missing the global optimum point and leading to oscillation around the point. Thus SBFA can be improved by applying adaptable chemotactic step size which could change: very large when bacteria are in locations far away from the global optimum location, to speed up the convergence, and very small when bacteria are in the locations near the global optimum so that bacteria able to find global optimum point without oscillation. Here, four novel adaptation schemes allowing the chemotactic step size to depending on the cost function value have been proposed. The adaptation schemes are developed based on linear, quadratic and exponential functions as well as fuzzy logic (FL). Then, the proposed BFAs with adaptable chemotactic step size, i.e. linearly adaptable BFA (LABFA), quadratic adaptable BFA (QABFA), exponentially adaptable BFA (EABFA) and fuzzy adaptable chemotactic step size (FABFA), are validated by using them to find global minimum point of seven well-known benchmark functions commonly used in development of optimisation techniques development. The results show that all ABFAs achieve better accuracy and speed compared to those of SBFA. The ABFAs are then used in modelling and control of a single-link flexible manipulator system. This includes modelling (based on linear model structures, neural network (NN), and fuzzy logic (FL)), optimising joint-based collocated (JBC) proportional-derivative (PD) control, and optimising both PD and proportional integral derivative (PID) control of end-point acceleration feedback for vibration reduction of a single-link flexible manipulator. The results show that ABFAs outperform SBFA in terms of convergence speed and accuracy. Since all SBFA and ABFAs use the same general parameters and bacteria are initially placed randomly across the nutrient media (cost function), the superiority better performance of ABFAs are attributed to the proposed adaptable chemotactic step size.
7
TIWARI, RAM MUKUND. « FUZZY EDGE DETECTION OF BLURRED IMAGE USING BACTERIA FORAGING ». Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14020.
Texte intégralRésumé :
This paper proposes an approach to edge detection of blurred color images. The edge detector involves two phases –Deblurring of color image using wavelet and edge detection using bacteria foraging. Here deblurring is performed without estimating the imge blur. The deblurring algorithm performs deblurring in the spectrum domain. In edge detection process, we find out the edge pixels on the basis of intensity difference value of pixel in their 8-neighbourhood. First step is Chemotaxis step in which we calculate the eight directional nutrients in the form of intensity difference and find out the edge pixels in the neighborhood of bacteria. Next in the Elimination and Dispersal step if a bacterium found itself low on nutrients than it will be eliminated from its current location and dispersed to some other location. Now if we trace all the edge pixels, given by the movement of bacteria than we will get an image highlighted with all the associated edges. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.
8
KUMAR, AJAY. « EDGE DETECTION USING BACTERIA FORAGING & ; FUZZY SIMILARITY MEASURE ». Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14024.
Texte intégralRésumé :
Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. Edges in images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image.In the proposed method, the bacteria foraging is used along with contemporary fuzzy logic which implements a relative pixel similarity value algorithm.The similarity between two pixels is calculated using the weighted participation of each fuzzy rule. Low similarity between the pixels represents the probability of a pixel to be an edge pixel. The bacteria moves over the low similarity region, thus maximizing the edge content while minimizing the presence of non-edge content in the movement path. Directional Pixel Similarity is used to locate the similar pixel to the edge pixel and thus the movement of bacteria is decided.Bacteria with sufficient nutrients are reproduced, i.e., at the intersection of more than one edge a bacterium will split into the number of edges. If a bacterium found itself low on nutrients than it will be eliminated from its current location and dispersed to some other location. The path traced by the bacteria is the edge map. Thus with proposed method, edge pixels in a color image are detected simultaneously without any complex calculations such as gradient, Laplace and statistical calculations.
9
Das, Saikishan, et K. Prasanna. « Multiple robot co-ordination using particle swarm optimisation and bacteria foraging algorithm ». Thesis, 2010. http://ethesis.nitrkl.ac.in/1886/1/B.Tech_Project_Thesis_Saikishan_Das(10603062).pdf.
Texte intégralRésumé :
The use of multiple robots to accomplish a task is certainly preferable over the use of specialised individual robots. A major problem with individual specialized robots is the idle-time, which can be reduced by the use of multiple general robots, therefore making the process economical. In case of infrequent tasks, unlike the ones like assembly line, the use of dedicated robots is not cost-effective. In such cases, multiple robots become essential. This work involves path-planning and co-ordination between multiple mobile agents in a static-obstacle environment. Multiple small robots (swarms) can work together to accomplish the designated tasks that are difficult or impossible for a single robot to accomplish. Here Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) have been used for coordination and path-planning of the robots. PSO is used for global path planning of all the robotic agents in the workspace. The calculated paths of the robots are further optimized using a localised BFA optimization technique. The problem considered in this project is coordination of multiple mobile agents in a predefined environment using multiple small mobile robots. This work demonstrates the use of a combinatorial PSO algorithm with a novel local search enhanced by the use of BFA to help in efficient path planning limiting the chances of PSO getting trapped in the local optima. The approach has been simulated on a graphical interface.
10
Lee, Kuo-Wei, et 李國維. « Improved Bacterial Foraging Optimization ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/22851452298832117486.
Texte intégralRésumé :
碩士大同大學
資訊經營學系(所)
101
This paper proposes an improved approach involving bacterial foraging optimization algorithm (BFOA) behavior. The new algorithm is called improved bacterial foraging optimization (IBFO). BFOA is a new swarm intelligence technique. Three main BFOA operation are chemotaxis, reproduction and elimination-dispersal, which are applied to global and local random searches. This powerful and effective algorithm has been used to solve various real-world optimization problem. However , BFOA has several shortages: many parameters needed to be set ; tumble angles are generated randomly and a fixed chemotactic step size causing poor convergence. In this paper, we try to improve these shortages of BFOA base on reduce setting parameters. Finally, we compare the performance of IBFO with the classical BFOA, testing them on seven widely-used benchmark functions. The experimental result shows that the IBFO is very competitive and outperforms the BFOA.
11
Lin, Guan-Yu, et 林冠喻. « Bacterial foraging for watermarkings applications ». Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66p8px.
Texte intégralRésumé :
碩士國立高雄大學
電機工程學系碩士班
97
In recent years, along with the booming application of the Internet, digital files and associated multimedia contents can be easily acquired in our daily lives. With the inherent characteristics of lossless copying and easy spreading, the intellectual property or ownerships of the multimedia contents have become a rising problem. Data hiding and watermarking techniques aiming at protecting copyright-related issues are of considerable interest in academia and industry. In this thesis, we mainly focus on improving the requirements of watermarking applications, including the watermark robustness and the invisibility in the frequency domain. Since the requirements tend to have conflicts, we employ bacterial foraging for training the watermarking algorithm and obtain the optimized solution. With the simulations presented, bacterial foraging provides a systematic way to balance the contributions by the watermarking requirements, and to offer another scope for designing an effective algorithm for watermarking.
12
Allemneny, Raghuveer. « Bacterial Foraging Based Channel Equalizers ». Thesis, 2006. http://ethesis.nitrkl.ac.in/24/1/raghuveer.pdf.
Texte intégralRésumé :
A channel equalizer is one of the most important subsystems in any digital
communication receiver. It is also the subsystem that consumes maximum computation
time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was
the most popular form of equalizer. Owing to non-stationary characteristics of the
communication channel MLSE receivers perform poorly. Under these circumstances
‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers
perform better.
Natural selection tends to eliminate animals with poor “foraging strategies” and favor the
propagation of genes of those animals that have successful foraging strategies since they
are more likely to enjoy reproductive success. After many generations, poor foraging
strategies are either eliminated or shaped into good ones (redesigned). Logically, such
evolutionary principles have led scientists in the field of “foraging theory” to
hypothesize that it is appropriate to model the activity of foraging as an optimization
process.
This thesis presents an investigation on design of bacterial foraging based channel
equalizer for digital communication. Extensive simulation studies shows that the
performance of the proposed receiver is close to optimal receiver for variety of channel
conditions. The proposed receiver also provides near optimal performance when channel
suffers from nonlinearities.
13
Cheng, Hsiu-Tzu, et 鄭秀姿. « Bacterial Foraging Optimization for Portfolio Optimizations ». Thesis, 2012. http://ndltd.ncl.edu.tw/handle/79562681397598793645.
Texte intégralRésumé :
碩士大同大學
資訊經營學系(所)
100
Portfolio optimization (PO) is a mixed quadratic and integer programming problem, and an effective solution approach is essential for most investors in order to raise expected returns and reduce investment risks. To solve this problem, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of bacterial foraging optimization algorithms (BFO) for solving the portfolio optimization problem. Bacterial foraging optimization algorithm is a new swarm intelligence technique and has successfully applied to some real world problems. Through three operations, chemotaxis, reproduction, and elimination and dispersal, the proposed BFO algorithm can effectively solve a PO problem with cardinality and bounding constraints. The performance of BFO approach was evaluated by performing computational tests on five benchmark data sets, and the computational results were compared to those obtained with existing heuristic algorithms. Experimental results demonstrate that the proposed algorithm is very competitive in portfolio optimization.
14
KUMARI, TANYA. « EDGE DETECTION USING BACTERIAL FORAGING AND UNIVERSAL LAW OF GRAVITY ». Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15059.
Texte intégralRésumé :
Edge detection is an important step used in various image processing algorithms. Edge gives the boundary of objects and provides information to separate the object from background or other overlapping objects. This work presents a new approach for edge detection inspired by Bacterial Foraging Algorithm (BFA) and Universal Law of Gravity. Among the many evolutionary algorithms proposed BFA is one of them. It consists of 4 stages viz chemotaxis, reproduction, swarming, elimination and dispersal. Chemotaxis stage decides the direction of movement of bacteria. Our aim is to move the bacteria through the edge pixels. The direction of movement of bacteria has been determined using the concept of Universal Law of gravity. The results of the proposed approach are compared with traditional edge detectors using different sample images. Quantitative analysis of the proposed approach with respect to traditional edge detectors has been done using Kappa and Entropy measures.
15
Yu, Chia-Jung, et 余家榮. « Design of Fuzzy PID Controllers Based on Modified Bacterial Foraging Optimization ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/21557765565511079873.
Texte intégralRésumé :
碩士國立高雄應用科技大學
電子工程系
98
In this thesis, we propose a modified bacterial foraging optimization (MBFO) approach, to include the synchronous bacterial foraging optimization (SBFO) and self adaptive bacterial foraging optimization (SABFO), to update the fuzzy proportional, integral, and derivative (FPID) gains which are optimized. Because of the genetic algorithm (GA) finds out the local optimal solution can not the global optimal solution for processing the complex problems. The bacterial foraging optimization (BFO) possesses chemotactic, swarming, reproduction, elimination and dispersal features to move the bacterial populations to the same environment, so this behavior avoid to get the local optimal solution. The modified bacterial foraging optimization by improving the moved length, position and objection function enhances the convergence speed of BFO. Hence, the modified bacterial foraging optimization gives more powerful improvements for the performance of control systems. Finally, from the demonstrating examples, the simulated results are presented to illustrate the better performance of the proposed methodology of (MBFO-FPID) approach compared with other approaches.
16
Chen, Kuan-Yu, et 陳冠宇. « Design of Adaptive Channel Equalizer Based on Modified Bacterial Foraging Optimization ». Thesis, 2011. http://ndltd.ncl.edu.tw/handle/62046402260337701178.
Texte intégralRésumé :
碩士國立高雄應用科技大學
電子工程系
99
In the communication systems, due to noise interference of signal transmission through channel, so the signal will need to be handled at the receiver, applying an equalizer recovering the received signal should be considered. Inter-symbol interference (ISI) is an important factor which affects the performance of communication systems, so that the originally transmitted symbols can be recovered correctly at the receiver, an equalizer can effectively eliminate ISI caused by band-limited channel or multipath. In this thesis, we propose a self adaptive bacterial foraging oriented by particle swarm optimization (SABF-PSO) approach, to update the adaptive channel equalizer weights which are optimized. Finally, from the simulation results are given to verify the effectiveness of the proposed method.
17
BANSAL, MINAL. « APPLICATION OF TYPE-2 FUZZY LOGIC TO REMOVE NOISE FROM COLOUR IMAGES BY USING BACTERIAL FORAGING ». Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14484.
Texte intégralRésumé :
Usually during acquisition or transmission of an image, it gets contaminated with the impulse
noise. In this paper, we present a novel application of type-2 fuzzy logic to the design of an
image processing operator called an impulse detector to remove this impulse noise from
images. The type-2 fuzzy logic based impulse detector can be used to guide impulse noise
removal filters to significantly improve their filtering performance and enhance their final
images. The structure of the proposed impulse detector is based on two 3-input and 1-output
first order Sugeno type, interval type-2 fuzzy inference system. The values of the internal
parameters of the type-2 fuzzy membership functions of the systems are determined by
experiment. When a noisy image is passed through the detector, its job is to determine which
of the pixels are noisy and which are not and then filter is applied on these noisy pixels to
output a restored image. Two advantages are addressed through the use of detector. One is,
not all the pixels of an image are noisy. So, it helps in recognizing the noisy pixels. Other is,
filter need not to be applied on complete image but is applied only on noisy pixels.
Application of filter on complete image degrades the quality of image. So, the usage of
detector before filtering helps in restoring the quality of image as well as it cuts cost. The
performance of the impulse detector is evaluated by using it in combination with median
impulse noise filter on four different popular test images under realistic noise condition of
30%. The results demonstrate that the type-2fuzzy logic based impulse detector can be used
as an efficient tool to effectively improve the performances of impulse noise filters and
reduce the impulse noise undesirable distortion effects. But it has been shown that better
performance can be expected if the internal parameters of the fuzzy inference system are
optimized. The internal parameters of the type-2 fuzzy membership functions of the systems
are determined by training. Bacterial Foraging Optimization Technique has been used for the
training purpose. In order to determine the ideal behaviour of the impulse detector, an ideal
detector has been developed which outputs the 100% accurate results which can be used by
the optimization algorithm for the computation purpose. Also, it helps in comparing the
results by the detector with and without optimization.
18
Chien, You-Ming, et 簡佑銘. « Application of Bacterial Foraging Algorithm for Fault Section Estimation of Power Systems ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/86489363430766542072.
Texte intégralRésumé :
碩士國立成功大學
電機工程學系碩博士班
96
The problem of fault section estimation is very important in power systems because effective fault section estimation will help facilitate the power restoration. Therefore, in this thesis, a Bacterial Foraging Algorithm (BFA) is proposed to solve the fault section estimation problem. The theme of this method is to simulate Escherichia coli such that the nutrients in the intestine can be better searched, with such a concept it is further employed to solve the fault section estimation problems. In order to validate the effectiveness of this approach, the method has been tested through different test scenarios with comparison to other methods. From the test results, they revealed the satisfactory computation performance of justifying the section of faults in a distribution system, thereby providing a useful reference as maintenance forewarning as well.
19
Chou, hsiaoping, et 周小萍. « Bacterial Foraging Particle Swarm Optimization Algorithm Time-Serial Fuzzy Prediction System Design ». Thesis, 2012. http://ndltd.ncl.edu.tw/handle/67897026614561251690.
Texte intégralRésumé :
碩士國立金門大學
觀光管理學系
100
This study presents Particle Swarm Optimization (PSO) and Bacterial Foraging Particle Swarm Optimization (BFPSO) to develop the financial time-series prediction system. The dynamic clustering-based learning algorithm is first to determine the number of Radial Basis Function Networks (RBFN) and select the center positions of Radial Basis Function. Thus, the initial system model is fast determined. The particle swarm optimization (PSO) and recursive least square (RLS) learning machines are proposed to acquire the appropriate parameters of model system to predict the behavior of the identified stock data set. In this article, the novel BFPSO learning algorithm is proposed to efficiently achieve the fuzzy system to create the accurate prediction model. The identified model can actually forecast the stock data. The real Taiwan Stock Price Index serial data between 2000 and 2004 illustrate the efficiency of the proposed learning scheme. Several experiments in the stock price prediction examples can present the powerful of our illustrated learning algorithms.
20
Lu, Wei-Cheng, et 呂偉誠. « Fuzzy Bacterial Foraging System and its Applications in Control of Servo Motors ». Thesis, 2011. http://ndltd.ncl.edu.tw/handle/72551615407317176764.
Texte intégralRésumé :
碩士國立臺灣師範大學
應用電子科技學系
99
This thesis proposes a modified bacterial foraging algorithm to adjust the design of fuzzy systems. Since traditional bacterial foraging algorithms require complicated operations and extremely time-consuming, the modified bacterial foraging algorithm utilize some simplified procedures to reduce the computation time and increase the operation efficiency. The simplified procedures include five parts that include: 1) modified Swarm Behavior, 2) electrification bacterium hover ability, 3) modified bacterium location, 4) adjustment of bacteria source, and 5) the best bacterium source evolution mechanism. The modified bacterial foraging algorithm is applied to update the parameters of fuzzy systems that approximate nonlinear functions, and to on-line tune the parameters of fuzzy controllers. The DC servo motor experiment and simulation results demonstrate the feasibility and applicability of the proposed methods.
21
Lin, Chih-chung, et 林志忠. « A probability matrix-based Discrete Bacterial Foraging Optimization for Capacity Vehicle Routing Problems ». Thesis, 2016. http://ndltd.ncl.edu.tw/handle/27872835340646727824.
Texte intégralRésumé :
碩士大同大學
資訊經營學系(所)
104
Vehicle routing problem (VRP) becomes more important in the distribution industry. Capacitated vehicle routing problems (CVRP) are a combinatorial optimization problem. It has been proved that CVRP is an NP-hard problem. Therefore it is hard to find optimal solutions for large-sized problems. This paper proposes a Discrete Bacterial Foraging Optimization with Probability Matrix (DBFOMAT) approach to solve the CVRP. The proposed approach uses a probability matrix as the main mechanism for solution matrix decoding. The algorithm first uses DBFO for assigning customers to routes and then uses probability matrices to arrange customer visiting sequences for each vehicle. This approach also uses another probability matrix to memorize customer sequences, which are used to guide customer sequencing in next iteration. The experimental results show that the proposed algorithm is an effective approach for solving the CVRP.
22
Lin, Yu-Sheng, et 林裕勝. « Bacterial Foraging Fuzzy Controllers and Its Application Study in Control of Two-Wheeled Vehicles ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/50443605853871296346.
Texte intégralRésumé :
碩士國立臺灣師範大學
應用電子科技研究所
101
This thesis focuses on adjusting the design of fuzzy control systems through a combination of the fuzzy control theory and the bacterial foraging algorithm. In addition, for a self-designed two wheeled vehicle, because of the reason that two-wheeled vehicle is unable to be self-balancing, a controller is required for forming a control system. The control system kernel is Megawin 82G516 single chip. The real-time data of angle and angular velocity are transmitted respectively from 3-axis accelerometer and gyroscope to the control kernel. Through the measurement amplifier processes analog signal, along with the construction of digital filter (Kalman filter) and fuzzy controller in the single chip, the control system kernel outputs a suitable pulse width modulation (PWM) to control the two-wheeled vehicles to go forward and backward, and even to make it self-balancing. The simulation results indicate that the two-wheeled unstable vehicle becomes stable immediately through the bacterial evolution. Moreover, the experiment results show that the two-wheeled vehicle with fuzzy bacterial evolution system can detect the gravity center of body such that the two-wheeled vehicle can move forward and backward.
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Huang, Chun-Yu, et 黃均瑜. « Design of Optimal Motion Controller for a Mecanum Wheeled Robot Using Bacterial Foraging Optimization ». Thesis, 2015. http://ndltd.ncl.edu.tw/handle/89209368293691613098.
Texte intégralRésumé :
碩士國立宜蘭大學
電機工程學系碩士班
103
This paper presents a bacterial foraging optimization algorithm (BFO) motion control method and its application to a four-wheeled Mecanum omnidirectional mobile robot. The optimal parameters of motion controller are auto-tuned by using the proposed BFO computing. By using Lyapunov stability theory, the closed-loop system has been proven stable. The convergent behavior of tracking errors provides better performance of the Mecanum omnidirectional mobile robot to achieve stability and trajectory tracking. The kinematic model is derived via the geometric architecture of Mecanum omnidirectional mobile robots and the BFO-based kinematic controller is developed. This efficient BFO-based intelligent motion controller is then implemented in a field programmable gate array (FPGA) chip by using the system-on-a-programmable-chip (SoPC) technology and hardware/software codesign to achieve stabilization and trajectory tracking. Simulations and experimental results demonstrate the effectiveness and merits of the proposed methods.
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Tung, Chih-Hauan, et 董至軒. « Automatic clustering for cell formation with alternative process routings using a Discrete Bacterial Foraging Optimization ». Thesis, 2014. http://ndltd.ncl.edu.tw/handle/4e62s3.
Texte intégralRésumé :
碩士大同大學
資訊經營學系(所)
102
The use of data clustering methods is one of the most popular approaches of designing cellular manufacturing systems (CMS). The aim of cell formation is to classify similar parts into part families and to group machines into manufacturing cells. In most past studies, the number of machine cells is known beforehand, but in practice there are many restrictions to obtain the number in advance. This study proposed an automatic clustering approach based on discrete bacterial foraging optimization (DBFO) algorithms to solve cell formation problems with alternative process routings. That is, the best number of machine cells is searched during the cell formation process, not given in advance. To enhance the searching ability of DBFO, the concept of gbest and pbest adopted from particle swarm optimization (PSO) is embedded into the proposed algorithm. Two types of generalized cell formation problems are tested: considering operation sequences and not considering operation sequences. The experimental results show that the proposed DBFO algorithm performs well for solving cell formation problems, particularly when the cell numbers are not given beforehand.
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Ting, Tsai You, et 蔡侑庭. « Application of Bacterial Foraging Optimization and GeneralRegression Neural Network on Taipei City House Prices Forecast ». Thesis, 2014. http://ndltd.ncl.edu.tw/handle/69331874993757814549.
Texte intégralRésumé :
碩士東吳大學
經濟學系
102
Abstract Having a warm home is the goal that we have to pursue, but the limited supply of land, susceptible to fluctuations in the impact of various external factors, but when faced with sudden upheaval, as previously the U.S. housing loan crisis or the Japanese real estate bubble event so big its influence can not be careless, so understanding the future trend of prices, has become the subject of government policy and people's lives are sustained attention. Known from research to explore existing literature, the use of artificial intelligence methods of forecasting models, often compared to traditional measurement methods alone can achieve better prediction. This article will introduce a new evolutionary computation method - bacterial foraging algorithm, hoping by bacterial foraging algorithm combined with generalized regression neural networks and other artificial intelligence methods to construct more effective predictive models. Empirical results indicate bacterial foraging algorithm model, in a small sample of prices in Taipei prediction model has better predictive ability; bacterial foraging algorithm to fine-tune and optimize through generalized regression neural network model parameters, Its predictions than other models available to smaller error rate, and effectively improve the accuracy of predictions. The study screened regularly post office savings variable is the one-year floating rate, M2, the consumer price index, the number of households and domiciled stock index and other five. And after training in the above model, can get good predictive value. This also explained the ups and downs of Taipei prices, mainly affected by the overall economic indicators. Keywords: house prices, artificial intelligence, neural networks, Bacterial
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Chen, Chieh-Han, et 陳玠含. « A Novel Strategy-adaptation-based Bacterial Foraging Optimization for FCMAC Model and Its Applications in Classification ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/06701788743677371159.
Texte intégralRésumé :
碩士國立勤益科技大學
資訊工程系
101
In this thesis, we propose a fuzzy cerebellar model articulation controller (FCMAC) model with improved bacterial foraging optimization (BFO). The proposed modified bacterial foraging optimization is called Strategy-adaptation-based Bacterial Foraging Optimization (SABFO). The SABFO is use to adjust the weights of FCMAC model and facilitate the actual output of the FCMAC model approximating the desired output for classifications. This thesis consisted of two major parts. In the first part, we propose a new Strategy-adaptation-based Bacterial Foraging Optimization. The main contribution of this study is adding the strategic approach into traditional bacterial foraging optimization. The propose method makes each bacterium perform different run-lengths, and increased bacterial diversity as well. We use nonlinear benchmark functions for verifying our propose SABFO to achieve the global optimal solution more easily than other methods. In the second part, we apply the propose SABFO to adjust the parameters of FCMAC model. Although back-propagation (BP) algorithm is commonly used to adjust the parameters of FCMAC model, it is easy to fall into the local optimal solution. Therefore, our proposed evolutionary algorithms can solve the above-mentioned optimal parameter problems successfully. Finally, the propose FCMAC model with SABFO learning algorithm is applied in classification problems. Experimental results demonstrate the convergence effectiveness of the proposed methods.
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Wei, Shih-Min, et 魏世旻. « Mobile Robot Wall-following Control Using a Fuzzy CMAC with Group-based Strategy Bacterial Foraging Optimization ». Thesis, 2015. http://ndltd.ncl.edu.tw/handle/20707906136341859210.
Texte intégralRésumé :
碩士國立勤益科技大學
資訊工程系
103
In this paper, a fuzzy cerebellar model articulation controller (FCMAC) with group-based strategy bacterial foraging optimization (GSBFO) for mobile robot wall-following control is proposed. In the proposed FCMAC controller, the inputs are the distance between the sonar and the wall, the outputs are the angular velocity of two wheels. Through using the GSBFO to adjust parameters of FCMAC. A new fitness function is defined to evaluate the evolution of mobile robot wall-following. The fitness function includes four assessment factors which are defined as follows: maintaining safe distance between the mobile robot and the wall, ensuring successfully running a cycle, avoiding mobile robot collisions, and mobile robot running at a maximum speed. The simulation results show that the improved BFO is better than the traditional BFO. After learning, mobile robot travels wall-following successfully in unknown environment.
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Farhat, Ibrahim A. « Economic and Economic-Emission Operation of All-Thermal and Hydro-Thermal Power Generation Systems Using Bacterial Foraging Optimization ». 2012. http://hdl.handle.net/10222/14865.
Texte intégralRésumé :
Electric power is a basic requirement for present day life and its various economic sectors. To satisfy the ever-increasing needs for electricity, the number of generating units, transmission lines and distribution systems is rising steadily. In addition, electric power systems are among the most complex industrial systems of the modern age. Beside complexity, the generation of electric power is a main source of gaseous emissions and pollutants. The planning and operation of electric power systems must be done in a way that the load demand is met reliably, cost-effectively and in an environmentally responsible manner. Practitioners strive to achieve these goals for successful planning and operations utilizing various optimization tools. It is clear that the objectives to be satisfied are mostly conflicting. In particular, minimizing the fuel cost and the gaseous emissions are two conflicting and non-commensurate objectives. Therefore, multi-objective optimization techniques are employed to obtain trade-off relationships between these incompatible objective functions in order to help decision makers take proper decisions.
In this thesis, two main power system operation problems are addressed. These are the economic load dispatch (ED) and the short-term hydro-thermal generation scheduling (STHTS). They are treated first as single-objective optimization problems then they are tackled as multi-objective ones considering the environmental aspects. These problems, single and multi-objective, are nonlinear non-convex constrained optimization problems with high-dimensional search spaces. This makes them a real challenge for any optimization technique. To obtain the optimal or close to optimal solutions, a modified bacterial foraging algorithm is proposed, developed and successfully applied. The bacterial foraging algorithm is a metaheuristic non-calculus-based optimization technique. The proposed algorithm is validated using diverse benchmark optimization examples before implementing it to solve the problems of this thesis. Various practical constraints are considered in the different cases of each problem. These include transmission losses, valve-point effects for both the ED and the STHTS problems and water availability and reservoir configurations for the STHTS problem. In all cases the optimal or near-optimal solution is obtained. For the multi-objective optimization cases, the Pareto optimal solution set that shows the trade-off relationship between the conflicting objectives is successfully captured.
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Shih, Po-Lin, et 施伯霖. « Integrated Optimal Energy Management/Gear Shifting Strategy Using Bacterial Foraging Algorithm for a Three-Power-Source Hybrid Powertrain ». Thesis, 2017. http://ndltd.ncl.edu.tw/handle/wdaw58.
Texte intégralRésumé :
碩士國立臺灣師範大學
工業教育學系
105
The purpose of this study is to develop the bacterial foraging algorithm (BFA) by applying it to the energy management/gear shifting strategy system of a three-power-source hybrid powertrain. Furthermore, this study was practical in nature, as it used the real-time simulation Hardware-in-the-Loop (HIL) to verify the algorithm’s feasibility. This study employs HIL to assess the influence that using BFA will have on the energy management and gear shifting strategy control of a three-power-source hybrid powertrain. The vehicle weighs 1,368 kilograms and its subsystems include a 43kW internal combustion engine, 30kW motor, 15kW integrated starter generator, and a 1.872kW-h Ah lithium battery. There are three primary steps for the energy management system and BFA energy management control: 1) chemotaxis, 2) reproduction, and 3) elimination-dispersal. The overall number of iterations was 30, and 80 bacteria were used carry out optimal energy management. BFA and two control strategies were used to carry out a comparison of fuel consumption with the NEDC (New European Driving Cycle) driving pattern. 1) Rule-based management: There are five control modes, which are system preparation, battery charging mode, electric mode, hybrid power mode, and extended range mode; the engineer used his experience to determine when to set and change modes. 2) Equivalent consumption minimization strategy (ECMS): By incorporating the global search algorithm (GSA), we searched for all the scope’s possibilities in order to find the most minimal fuel consumption for power distribution ratio and gear shifting strategy. At the end of the study, we used HIL to simulate the feasibility and verify fuel consumption benefits of BFA on vehicle control units (VCU) in real time. A basic rule base, ECMS, BFA, and real-time were the four conditions for the equivalent consumption with the NEDC driving pattern: 538.9g, 209.6g, 248.9g, and 253.6g were their respective values. The equivalent consumption values with a FTP-72 driving cycle were 579.2g, 291g, 316.3g, and 320.38g. ECMS, BFA, and real-time were compared with a basic rule base when using a NEDC driving pattern to determine percentage values for improvement in energy consumption: 61%, 53.8%, and 52.9%. Percentage values for improvement in energy consumption for a FTP-72 driving cycle were 49.7%, 45.3%, and 44.6%. The improvement in equivalent consumption values for BFA and real-time for the NEDC driving pattern and FTP-72 driving cycle were 98% similar, and they were only outperformed by ECMS, which was the optimal solution. In the future, this experiment will be used to test a three-power-source e-CVT hybrid-powered vehicle.
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WANG, CHUN-HUNG, et 王俊弘. « Adaptive Iterative Learning Control for Freeway Traffic Flow Systems Using Improved Bacterial Foraging Optimization based Desired Traffic Density Planning ». Thesis, 2017. http://ndltd.ncl.edu.tw/handle/7d739m.
Texte intégralRésumé :
碩士華梵大學
電子工程學系碩士班
105
For iterative learning control problem of unknown robotic systems with initial resetting errors, we propose a discrete fuzzy-neural adaptive iterative learning control (AILC) for freeway traffic flow systems with random initial resetting errors , iteration-varying desired traffic densities and random bounded off-ramp traffic volumes using traffic densities, space mean speeds and on-ramp waiting queues design. It is assumed that the system nonlinear functions and input gains are unknown for controller design. An adaptive fuzzy neural network (FNN) controller and an adaptive robust controller are applied to compensate for the unknown system nonlinearities and input gains, respectively. Moreover, to deal with the disturbances from random bounded off-ramp traffic volumes, a dead zone like auxiliary error with a time-varying boundary layer is introduced as a bounding parameter. This proposed auxiliary error is also utilized to construct the adaptive laws without using the bound of the input gain. The traffic density tracking error is shown to converge along the axis of learning iteration to a residual set whose level of magnitude depends on the width of boundary layer. Besides, since the nice desired traffic densities designed for the coordinated control objective of the AILC for freeway traffic flow systems are generally unknown, the improved bacterial forging optimization (IBFO) algorithm is used to optimize the fitness function, which is constructed by the coordinated control objective and includes (1) minimum total travel time, (2) minimum on-ramp average waiting time, and (3) minimum changes of desired traffic densities. Finally, a computer simulation example is used to verify the learning performance of the proposed fuzzy-neural AILC for freeway traffic flow systems using IBFO based desired traffic densities planning.
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Huang, Jin-Yuan, et 黃進源. « The Study of hyperspectral imaging on Paddy Rice Image Classification through Comparison of Back-Propagation Neural Network and Bacterial Foraging Optimization ». Thesis, 2016. http://ndltd.ncl.edu.tw/handle/cear4w.
Texte intégralRésumé :
碩士嶺東科技大學
資訊管理系碩士班
104
The hyperspectral is an advanced material which render more accurately classification result. However, due to spectral information is very rich, the computation also takes more time. Therefore, if we can construct a decision system to accurately interpreting the surface of ground, it can be applied significantly to reduce manpower and time. This study focused on how to extract the important factors of hyperspectral image to classify the paddy rice area with applying supervised and unsupervised learning algorithm. In this study, back-propagation neural network and bacterial foraging optimization for hyperspectral image for image classification. The prior processing of image data used entropy-based classification to extract the influenced factors of image band properties. Then the two algorithms are applied into following four case studies: (a) the original band with a back-propagation neural network (b) entropy-base-classification filter out important information with back-propagation neural network (c) the original band with bacterial foraging optimization (d) entropy-base-classification filter out important information with bacterial foraging optimization. Finally, the error matrices are present and thematic maps are drawn among four cases and outcomes are compared.
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YEN, HAN-TAO, et 顏翰濤. « The Study of Hyperspectral Imaging on Paddy Rice Image Classification through Particle Swarm Optimization with Linear Discriminant Analysis and Bacterial Foraging Optimization ». Thesis, 2016. http://ndltd.ncl.edu.tw/handle/hdg4nb.
Texte intégralRésumé :
碩士嶺東科技大學
資訊管理系碩士班
104
This study focused on how to extract the important from hyperspectral imaging spectral information. The techniques of image classification on paddy fields with supervised and unsupervised learning approaches. In this study, Linear Discriminant Analysis and bacterial foraging optimization for hyperspectral imagery for image classification. The preprocessing of Particle Swarm Optimization is used to extract the important influenced factor, and then design into the following four different case studies: ( a) the original band with Linear Discriminant Analysis (b) Particle Swarm Optimization filter out important information with Linear Discriminant Analysis (c) the original band with bacterial foraging optimization (d) Particle Swarm Optimization filter out important information with bacterial foraging optimization, finally using the error matrix and topic maps showing the results of the classification after comparison.