Дисертації з теми "Neural-genetic algorithm"
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Tong, Dong Ling. "Genetic algorithm-neural network : feature extraction for bioinformatics data." Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15788/.
Повний текст джерелаBlomström, Karl. "Benchmarking an artificial neural network tuned by a genetic algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-58253.
Повний текст джерелаMurnion, Shane D. "Neural and genetic algorithm applications in GIS and remote sensing." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337024.
Повний текст джерелаMcMurtrey, Shannon Dale. "Training and Optimizing Distributed Neural Networks Using a Genetic Algorithm." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/243.
Повний текст джерелаReiling, Anthony J. "Convolutional Neural Network Optimization Using Genetic Algorithms." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.
Повний текст джерелаKopel, Ariel. "NEURAL NETWORKS PERFORMANCE AND STRUCTURE OPTIMIZATION USING GENETIC ALGORITHMS." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/840.
Повний текст джерелаMacLeod, Christopher. "The synthesis of artificial neural networks using single string evolutionary techniques." Thesis, Robert Gordon University, 1999. http://hdl.handle.net/10059/367.
Повний текст джерелаDeane, Jason. "Scheduling online advertisements using information retrieval and neural network/genetic algorithm based metaheuristics." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015400.
Повний текст джерелаFischer, Manfred M., and Yee Leung. "A Genetic Algorithm Based Evolutionary Computational Neural Network for Modelling Spatial Interaction Data." WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/4151/1/WSG_DP_6198.pdf.
Повний текст джерелаSeries: Discussion Papers of the Institute for Economic Geography and GIScience
Stivason, Charles T. "Industry Based Fundamental Analysis: Using Neural Networks and a Dual-Layered Genetic Algorithm Approach." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/40422.
Повний текст джерелаPh. D.
Buys, Stefan. "Genetic algorithm for Artificial Neural Network training for the purpose of Automated Part Recognition." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1008356.
Повний текст джерелаCheng, Martin Chun-Sheng, and pjcheng@ozemail com au. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030722.172812.
Повний текст джерелаKothari, Bhavin Chandrakant. "Structural optimisation of artificial neural networks by the genetic algorithm using a new encoding scheme." Thesis, Brunel University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389263.
Повний текст джерелаZhang, Xiaohui. "Development and Testing of a Combined Neural-Genetic Algorithm to Identify CO2 Sequestration Candidacy Wells." Thesis, University of Louisiana at Lafayette, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1594272.
Повний текст джерелаThis study was motivated by how to use statistical tool to identify the candidacy wells for CO2 Capture and Sequestration based on the idea of using Artificial Neural Networks to predict the leakage index of a well. A Combined Neural-Genetic Algorithm was introduced to avoid BP neural network getting a local minimum because Genetic Algorithm simulates the survival of the fittest among individuals over consecutive generation. Based on the algorithm, 1356 lines of C code were composed using Microsoft Visual Studio 2010. The Combined Neural-Genetic Algorithm developed in this thesis is able to handle large size of data sample with at least 10 factors. Several parameters were considered as factors that may have an effect on the performance of Combined Neural-Genetic Algorithm, including the population size, max epoch, error goal, probability of crossover, probability of mutation, number of neurons in hidden layer, number of factors and size of data sample. The accuracy of the BP neural network and the CPU time are the two major parameters to evaluate the performance of the Combined Neural-Genetic Algorithm. A sensitivity analysis was performed to identify the effect these factor have on the performance. Based on the result of the sensitivity analysis, some recommendations are provided about initializing these factors.
Spittle, Mark Charles. "Complexity reduction in artificial neural networks with an emphasis on genetic algorithm based optimisation techniques." Thesis, Cardiff University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389853.
Повний текст джерелаDehaven, Ryan Swords. "Smarter NEAT Nets." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1024.
Повний текст джерелаLeong, Sio Hong. "Kinematics control of redundant manipulators using CMAC neural networks combined with Descent Gradient Optimizers & Genetic Algorithm Optimizers." Thesis, University of Macau, 2003. http://umaclib3.umac.mo/record=b1446170.
Повний текст джерелаSahebi, Mahmod Reza. "Understanding microwave backscattering of bare soils by using the inversion of surface parameters, neural networks and genetic algorithm." Thèse, Université de Sherbrooke, 2003. http://savoirs.usherbrooke.ca/handle/11143/2736.
Повний текст джерелаLiu, Jingrong. "Design and Analysis of Intelligent Fuzzy Tension Controllers for Rolling Mills." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/848.
Повний текст джерелаSabih, Ann Faik. "Cognitive smart agents for optimising OpenFlow rules in software defined networks." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15743.
Повний текст джерелаLi, Keh-Tsong, and 李克聰. "Neural Network combined with Genetic Algorithm-Evolutionary Neural Network." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/37236508646662658444.
Повний текст джерела國立交通大學
電機與控制工程系
87
This thesis presents a Real-Coded Rank-Based Genetic Algorithm (RCRBGA), which is represented by a chromosome containing parameters in floating-point. The use of rank-based fitness increases the population diversity. The offspring are generated by the rank-based reproduction, real parametric crossover and mutation in the evolving process. Besides, an Evolutionary Neural Network (ENN) which combines RCRBGA and Back-Propagation (BP) is introduced. ENN applies the learning concept to the evolution process, like the behavior of human beings. It not only improves the disadvantage of easily slumping in to local minima of BP but also overcomes the defect of genetic algorithm, which can't efficiently converge to minima. Finally, the search ability of RCRBGA is demonstrated by an example, linear state-feedback controller via pole-assignment method. In addition, ENN applies to a classifying problem of the modified XOR to show its advantage.
Chen, lily, and 陳麗莉. "A Neural Genetic Algorithm for Product Design." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/76419743128612100659.
Повний текст джерелаFong-Hang, Liao, and 廖鴻翰. "Construct Neural Network Model Using Genetic Algorithm." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/60094674695662600817.
Повний текст джерела大葉大學
電機工程研究所
86
In this thesis a new Genetic Algorithm to optimize weights and topology of Neural Networks is presented and compared with other learning methods, such as gradient-descent learning algorithm, and other evolutionary system. Since the characteristics of topology space and weight space (one of them is in integer space and the other is in real space) are absolutely different, it is very difficult to optimize both of them at the same time. Cascade-Correlation algorithm (CCA) is a popular supervised learning architecture that dynamically grows layers of hidden neurons, so that the network topology (size, depth) can be determined with network weights at the same time. CCA is a gradient-descent based learning algorithm. It is known that the gradient methods always foiled by local minimum problem. On the other hand, it is powerful on local search but insufficient on global search. Genetic algorithm (GA) is a computationally intensive optimization method. The rewards from the huge computational power are some very desirable properties. One of them is that a global search is performed during the optimization. But unfortunately, GA is insufficient on local search for the reason of poor fine-tuning. In order to optimize network topology and weights more efficiently. It is possible to combine the advantages of them (CCA and GA), and avoid the disadvantages. For the reason, Genetic Algorithm Based Correlation-Construction (GABCC) was developed. The basic concept of GABCC is ''adaptability''. By adaptive GA operators(selection, crossover, mutation), we can evaluate the population more efficiently. Adaptive GA operators have good abilities both on global search and local search. For the reason, it gets better performance than traditional gradient methods shown in the benchmarks we have tested. More over, GABCC combine both of the adaptive GA and correlation-constructed method. So that weights and topology of network can be optimized at the same time
Hsu, Tung-Jung, and 許東榮. "Integrating Genetic Algorithm with Neural Network for." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/37047274475860922509.
Повний текст джерела國立勤益科技大學
工業工程與管理系
101
Taiwan, which possesses cutting-edge industries, lacking for natural resources, and dominating the field in semiconductors, optoelectronics, information, communications, electronics precision manufacturing technology. In recent years, with the rapid growth of international trade and the competitive environment, inbound and outbound passengers and volume of imported goods are increasing. In addition to execution levied on tariffs and preventing smuggling, Customs officers must perform border control measures, such as national security, quarantine, environmental protection, protection of intellectual property rights, etc. Using scientific management methods, construction of preventing smuggling expert system, knowledge management, and purchasing high-tech equipment can relieve customs officers on tasks burden. This study integrated genetic algorithms with neural network to construct a smuggling case classification and import tariffs prediction model which provide Customs expert system a decision-making tool and enhance the performance.
Chen, Tsung-Hung, and 陳宗宏. "Neural networks assess liquefaction of sand -Genetic algorithm." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/44542373651175737769.
Повний текст джерелаLee, I.-Ting, and 李宜庭. "Evolution of Neural Circuit Models by Genetic Algorithm." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/bx28bc.
Повний текст джерелаLi, Hai-Han, and 李海涵. "An Improved Algorithm Applied in Training Neural Network-Combined with Genetic Algorithm." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/et2442.
Повний текст джерела國立臺北科技大學
商業自動化與管理研究所
93
Gradient steepest descent (GSD) is often used to train the back-propagation neural network (BPN) because of its excellent performance of reducing training errors; however, it also has some drawbacks such as slow convergence and local optimum problem. Many improved methods are proposed to amend the aforementioned demerits; for example, momentum can be employed to accelerate convergence, and global search methods, e.g. probabilistic climbing search and taboo search (TS), etc. are introduced to fix the local optimum problem. Nevertheless, some weaknesses exist in those methods. For instance, added momentum may sometimes not work well in speeding up convergence; probabilistic climbing methods assume that error function follows a certain distribution, which may not always be true. While TS might approximate the global solutions, its quality of solution remains unstable on account of too many random variables and it often requires heavy computation. This paper proposes an improved method to hasten convergence and decrease the training errors effectively without much more training time. Even so, whatever algorithms which are mentioned above encounter bottleneck of achieving more accuracy of training. That is, diminishing of training errors becomes stagnant at some convergence level. If evolutionary algorithms e.g. genetic algorithm (GA) is combined, training accuracy may be in theory refined indefinitely to its maximum precision with proper evolving strategies. This paper lays emphasis on evolving strategies instead of evolving operators. It’s preliminarily proved in this paper that during the long evolution process, influence of evolving strategies is greater than that of evolving operators. The training time of combining GA would not grow as a result of parallel processing.
Yang, Guo-Feng, and 楊國鋒. "Face Detection Using Genetic Algorithm and Artificial Neural Network." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/18758722183870591957.
Повний текст джерела元智大學
資訊工程學系
96
Human face represents one of the most common patterns in our vision. Therefore, automatic recognition of human faces is an essential task in many applications such as criminal identification and security checks. The first important step of automatic human face recognition is to detect face in a given unknown picture. However, the task of automatic face detection in a complex background is difficult to cope with. In this thesis, discriminating features are selected by genetic algorithm with neural network so as to design an accurate face detector. Moreover, verification on face skin has been involved to increase the accuracy of face detection. Experimental results prove the effectiveness of the proposed text detection method.
Feng, Yen-Ru, and 馮彥儒. "Text Detection Using Genetic Algorithm and Artificial Neural Network." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/62282555625352186744.
Повний текст джерела元智大學
資訊工程學系
96
The text embedded in images and video streams imply tremendous information. Thus, text extraction from image or video streams has been widely applied in a variety of application fields, such as document analysis, content-based retrieval and intelligent transportation system, etc. However, texts are often embedded in an image and may vary in language, font, size, and deformation, which, in turn enhance the difficulty of text detection problem. In this thesis, discriminating features are selected by genetic algorithm with neural network. Moreover, fusion of pyramid images has been involved to detect texts in variation of sizes. Experimental results prove the effectiveness of the proposed text detection method.
Ying-Yi, Wang. "A Hybrid Neural-genetic Algorithm for Reservoir Water Quality Management." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2007200610412000.
Повний текст джерелаShia, Yu-Lung, and 夏裕龍. "Apply Genetic Algorithm And Neural Network To Forecast Taiwan Weather." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/78336942646795301517.
Повний текст джерелаChang, Chia-Tsang, and 張家瑲. "Application of Neural Network and Genetic Algorithm to System Identification." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/73351083036139978352.
Повний текст джерела朝陽科技大學
營建工程系碩士班
91
Taiwan is a high seismic zone since it is located at the active arc-continent collision region between the Luzon arc of the Philippine Sea plate and the Eurasian plate. The Chi-Chi Earthquake is the largest inland earthquake occurred in Taiwan during this century. Due to the great damage caused by this earthquake, more and more emphases have been put on the earthquake resistant design of buildings. Dynamic behavior of buildings under earthquakes should be considered in the process of design. In order to realize the dynamic behavior of structural systems subjected to earthquakes, we can determine dynamic models and parameters through various system identification techniques. In this study, it is intended to develop new identification techniques by combining the advantages of both neural network (NN) and genetic algorithm (GA). Firstly, the time history of the ground acceleration and the system parameters of a variety of SDOF systems are used as the input data of neural network, and the time history of the relative acceleration of the respective SDOF systems as the neural network outputs. After the training of the neural network, the network topology used to evaluate the time history of the relative acceleration of the SDOF systems will be captured. This network topology is then employed to replace the procedure for solving the governing (differential) equation when GA is used to identify the system parameters. Furthermore, this topology is used in the identification of the MDOF system subjected to the single input by mode superposition technique. On the other hand, the starting weights of NN are randomly selected and the optimization algorithm used in the training of NN may get stuck in the local minimal. GA is a search method based on natural selection and genetics and is different from conventional optimization methods in several ways. The GA is a parallel and global search technique that searches multiple points, so it is more likely to obtain a global solution. In this regard, a new algorithm of combining GA and NN is proposed here. The GA is employed to search for the starting weights and the NN is used to obtain the network topology. Through the iterative process of selection, reproduction, cross over and mutation, the optimal weight can then be obtained. This proposed algorithm is applied to the Duffing oscillator and Wen’s degrading nonlinear systems. Finally, the accuracy of this method is illustrated by comparing the results of the predicted response with the measured one.
Wang, Ying-Yi, and 王英義. "A Hybrid Neural-genetic Algorithm for Reservoir Water Quality Management." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/87142282187697168993.
Повний текст джерела國立臺灣大學
土木工程學研究所
94
There has been concern over the water quality in Feitsui Reservoir, particularly since the beginning of Taipei-Ilan highway construction in 1991. In the present study, a combined artificial neural network (ANN) and genetic algorithms (GAs) approach was proposed for water quality management of Feitsui Reservoir in Taiwan. First, two simplified water quality models based on ANN were developed and used as universal approximators to imitate the cause-and-effect relationships between phosphorus loads from the watershed and water quality concentrations (total phosphorus and chlorophyll a, respectively) in Feitsui Reservoir. A six-year (1992-1997) record of water quality data was used for network training, and additional data collected in 1998-2000 was used for model verification. The performance and validity of the proposed ANN models were evaluated using two conventional water quality models, including a total phosphorus model and an eutrophication model (WASP/EUTRO). Further, a GA with water quality prediction produced by the ANN model was used to optimize the control of watershed nutrient loads. The GA was applied to the problem of reservoir water quality management to provide an alternative when searching for an optimal control strategy. The study results reveal that the ANN model can effectively simulate the dynamics of reservoir water quality, indicating that an ANN model can replace the conventional water quality model in this water quality management analysis, and the GA is able to identify control schemes that improve the current trophic levels to achieve water quality standards. Finally, the time-variable control schemes derived from the ANN-GA method were applied to the WASP/EUTRO model to assess the impact on eutrophication in Feitsui Reservoir following phosphorus load reductions in its watershed. The modeling results suggest that adequate control of phosphorus loads into the reservoir is needed for preserving the water quality of Feitsui Reservoir from eutrophication. In practice, the time-varying reductions in phosphorus loads for controlling reservoir eutrophication can be achieved by way of the joint reduction of point and nonpoint source pollution loads.
Huang, Shin-Mao, and 黃鑫茂. "A Novel Neural Network Training Technique by Using Genetic Algorithm." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/75933188485584344214.
Повний текст джерела國立交通大學
電機與控制工程系
88
This thesis investigates a novel neural network training technique, which employs the genetic algorithm to finding the initial values of the neural network. It is represented by a chromosome containing parameters in floating-point, so that the convergence rate to the minima becomes faster. This hybrid algorithm can overcome not only the drawback of easily slumping into local minima of back-propagation but also the genetic algorithm’s defect that can’t efficiently converge to the minima of the neighborhood. Further, the thesis shows that a gene changing one by one is better than that changing totally at once. Finally, the results of computer simulations reveal that this algorithm has a better convergence property, the time of global searching is obviously decreased.
Chang, Chen-Chi, and 張錦基. "Measuring body fat using regression analysis﹐artificial neural network and genetic algorithm neural network." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/60672346971399257994.
Повний текст джерела淡江大學
資訊工程學系碩士在職專班
98
Body fat mass is one of the health indicators. Measuring it is helpful to understand the relationship between body fat and diseases. Although, cadaver dissection provides the most accurate method to assess the value. But, it is not appropriate for the people who are living. Additionally, some accurate methods, such as underwater weighting, isotope dilution, bioelectrical impedance analysis , are complicated and costly incredibly. Therefore, Young Men''s Christian Association (YMCA) and the United States army tried to develop instruments for gauging body fat. Furthermore, World Health Organization (WHO) suggested that using body mass index (BMI) instead of body fat. However, evaluating BMI is not considered distribution of human body fat tissue component and specific region. It is doubtful about the accuracy. The purpose of this study is constructing a more precise predict model by multiple regression analysis, artificial neural network, genetic algorithm neural network, the parameters are age, weight, height, neck circumference, chest circumference, abdomen circumference, hip circumference, and so on. 252 males’ body measurement indicators were database which were collected by Dr. A. Garth Fisher who was in Human Performance Research Center , Brigham Young University , Provo. The result is genetic algorithm neural network RMSE: Root Mean Square Error (RMSE 4.0854) > artificial neural network 5 variables model (RMSE 4.3330) > artificial neural network 12 variables model (RMSE 4.3783) > multiple regression analysis 12 variables model (RMSE 4.3981) > multiple regression analysis 5 variables model (RMSE 4.4620) > YMCA body fat model (RMSE 4.7757) > US Army body fat Model (RMSE 7.7336)。
Hippolyte, Djonon Tsague. "MACHINE CONDITION MONITORING USING NEURAL NETWORKS: FEATURE SELECTION USING GENETIC ALGORITHM." Thesis, 2007. http://hdl.handle.net/10539/2127.
Повний текст джерелаCondition monitoring of machinery has increased in importance as more engineering processes are automated and the manpower required to operate and supervise plants is reduced. The monitoring of the condition of machinery can significantly reduce the cost of maintenance. Firstly, it can allow an early detection of potential catastrophic fault, which could be extremely expensive to repair. Secondly, it allows the implementation of conditions based maintenance rather than periodic or failure based maintenance [1]. In these cases, significant savings can be made by delaying schedule maintenance until convenient or necessary. Although there are numerous efficient methods for modeling of mechanical systems, they all suffer the disadvantage that they are only valid for a particular machine. Changes within the design or the operational mode of the machine normally require a manual adaptation. Using Neural Networks to model technical systems eliminates this major disadvantage. The basis for a successful model is an adequate knowledge base on which the network is "trained". Without prior knowledge of the machines systematic behavior or its history, training of a neural Network is not possible. Therefore, it is a pre-requisite that the knowledge base contains a complete behavior of the machine covering the respective operational modes whereby, not all rather the most important modes are required. Neural networks have a proven ability in the area of nonlinear pattern classification. After being trained, they contain expert knowledge and can correctly identify the different causes of bearing vibration. The capacity of artificial neural networks to mimic and automate human expertise is what makes them ideally suited for handling nonlinear systems. Neural networks are able to learn expert knowledge by being trained using a representative set of data [2]-[6]. At the beginning of a neural network’s training session, the neural network fault detector’s diagnosis of the motor’s condition will not be accurate. An error quantity is measured and used to adjust the neural network’s internal parameters in order to produce a more accurate output. This process is repeated until a suitable error is achieved. Once the network is sufficiently trained and the parameters have been saved, the neural network contains all the necessary knowledge to perform the fault detection. One of the most important aspects of achieving good neural network performance has proven to be the proper selection of training features. The curse of dimensionality states that, as a rule of thumb, the required cardinality of the training set for accurate training increases exponentially with the input dimension [7]. Thus feature selection which is a process of identifying those features that contribute most to the discrimination ability of the neural network is required. Proposed methods for selecting an appropriate subset of features are numerous [8]-[11]. Methods based on generating a single solution, such as the popular forward step wise approach, can fail to select features which do poorly alone but offer valuable information together. Approaches that maintain a population of solutions, such as genetic algorithms (GA) are more likely to speedily perform efficient searches in high dimensional spaces, with strong interdependencies among the features. The emphasis in using the genetic algorithm for feature selection is to reduce the computational load on the training system while still allowing near optimal results to be found relatively quickly. To obtain accurate measure of the condition of machinery, a wide range of approaches can be employed to select features indicative of condition. By comparing these features with features for known normal and probable fault conditions, the machine’s condition can be estimated. The most common approach is that of analysis in the frequency domain by applying a Fast Fourier Transform (FFT) to the time domain history data. The idea is simply to measure the energy (mean square value) of the vibrations. As the machine condition deteriorates, this measure is expected to increase. The method is able to reveal the harmonics around the fundamental frequency of the machine and other predominant frequency component (such as the cage frequency) [12]. Frequency analysis is well established and may be used to detect, diagnose and discriminate a variety of induction motor faults such as broken rotor bars, cage faults, phase imbalance, inner and outer race faults. However, as common in the monitoring of any industrial machine, background noise in recorded data can make spectra difficult to interpret. In addition, the accuracy of a spectrum is limited due to energy leakage [12- 14]. Like many of the new techniques now finding application in machinery condition monitoring, Higher Order Statistics was originally confined to the realms of non-linear structural dynamics. It has of recent however found successful application to the identification of abnormal operation of diesel engines and helicopter gearboxes [5, 7]. Higher Order Statistics provide convenient basis for comparison of data between different measurement instances and are sufficiently robust for on-line use. They are fast in computation compared with frequency or time-domain analysis. Furthermore, they give a more robust assessment than lower orders and can be used to calculate higher order spectra. This dissertation reports work which attempts to extend this capability to induction motors. The aim of this project is therefore to examine the use of Genetic Algorithms to select the most significant input features from a large set of possible features in machine condition monitoring contexts. The results show the effectiveness of the selected features from the acquired raw and preprocessed signals in diagnosis of machine condition. This project consists of the following tasks: #1; Using Fast Fourier transform and higher order signals techniques to preprocess data samples. #1; Create an intelligent engine using computational intelligence methods. The aim of this engine will be to recognize faulty bearings and assess the fault severity from sensor data. #1; Train the neural network using a back propagation algorithm. #1; Implement a feature selection algorithm using genetic algorithms to minimize the number of selected features and to maximize the performance of the neural network. #1; Retrain the neural network with the reduced set of features from genetic algorithm and compare the two approaches. #1; Investigate the effect of increasing the number of hidden nodes in the performance of the computational intelligence engine. #1; Evaluate the performance of the system using confusion matrices. The output of the design is the estimate of fault type and its severity, quantified on a scale between 0-3. Where, 0 corresponds to the absence of the specific fault and 3 the presence of a severe machine bearing fault. This research should make contribution to many sectors of industry such as electricity supply companies, and the railroad industry due to their need of techniques that are capable of accurately recognizing the development of a fault condition within a machine system component. Quality control of electric motors is an essential part of the manufacturing process as competition increases, the need for reliable and economical quality control becomes even more pressing. To this effect, this research project will contribute in the area of faults detection in the production line of electric motor.
Chen, Chi-Wei, and 陳啟瑋. "Hybrid Genetic Algorithm and Neural Network to Design A PID Controller." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/91856841436605951017.
Повний текст джерела國立臺灣海洋大學
機械與機電工程學系
98
The PID controller operation is simple and easy to design, and it has been used widely in industrial applications. The performance of controller depends on the control parameters. As the PID controller parameter set dependence of experience or experiment to determine, it is difficult to get the best parameters. The back-propagation neural network uses the steepest gradient decent method to adjust weights. The initial weights of neural network generate by random or experience. This will cause time-consuming and poor reliability. This paper proposes a genetic algorithm to optimize the parameters by the selection, crossover and mutation. Through the set of fitness function, and find the best initial weight in the system. According to the simulation results, hybrid genetic algorithm and neural network PID controller can self-adjust the parameters, and both have good robustness and adaptability on linear and nonlinear systems.
Shen, Tzung-Tza, and 沈宗澤. "Training Artificial Neural Network Using Genetic Algorithm and Conjugate Gradient Method." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/18262883491045855458.
Повний текст джерела國立成功大學
航空太空工程學系
89
The purpose of this study is to combine the conjugate gradient method(CG) and the genetic algorithm(GA) for the training of artificial neural networks(ANN). The back-propagation artificial neural network is a broadly used artificial neural network in many areas. It usually adopts the steepest descent method(SD) to search for a set of connection weights that minimizes the training error. But the convergence of the steepest descent method is very slow and easy to trap into a local optimal. In order to speed up the convergence, the conjugate gradient method searches the optimal weights along a set of conjugate directions in stead of steepest descent ones. But it still has the drawback of trapping into local optimals. The genetic algorithm is a global optimization method based on the Darwin’s principle of ‘’Survival of the fittest’’. The genetic algorithm always searches for the global optimal. In this study, we develop a hybrid method which combines the conjugate gradient method and the genetic algorithm to improve the convergence and successful rate for the training of artificial neural networks.
Lee, Jung-Che, and 李榮哲. "Implementation of FPGA-Based Artificial Neural Network Combined with Genetic Algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/92274078038329124679.
Повний текст джерела國立交通大學
電控工程研究所
100
This thesis is aimed to implement the hardware structure of the genetic algorithm (GA), which is applied to search the optimal weights for the FPGA-based artificial neural network (ANN). In contrast with the traditional gradient algorithm, GA uses multi-point population to search the optimum, which is suitable to implement on FPGA in binary code without complex computation. There are two modules proposed for GA hardware to speed up searching, CMU and SU. The CMU generates one crossover mask and two mutation masks at the same time, not in order, to reduce a lot of execution clock cycles. The SU finds the best individual in each generation and saves it as the next generation parent to always keep the elite in the population. The hardware includes three crossover operations, one-point crossover, two-point crossover and uniform crossover. The users can choose one of them and define the crossover rate and mutation rate to deal with different problems. As for the forward calculation of ANN, the multilayer architecture is realized by the layer multiplexing method to reduce the resource since it only requires a single layer to be used repeatedly. The success of the GA hardware architecture is demonstrated by three experiments on Altera DE2-70 FPGA board with 50 MHz operation frequency, including two-dimensional optimal searching, M-G curve prediction fitting and edge detection.
LAI, I.-CHIEN, and 賴以建. "GENETIC ALGORITHM, NEURAL NETWORK AND DECISION TREE IN PRE-WARNING MODELS." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/34939994824622402263.
Повний текст джерела國立臺北大學
企業管理學系
91
In the past, the pre-warning models for Financial Crisis are usually established based on traditional statistical methods such as Discriminant Analysis. However, it is often questionable whether the financial data satisfies the assumptions of such models. Therefore, this study investigates the construction of pre-warning model through nonlinear methods such as Genetic Algorithm and Neural Network. In additional, since the reference value for the key indicator that influences business failure most cannot be extracted from the pre-warning model, this study starts with using Decision Tree technique to extract this reference value. Based upon this, the objectives of this thesis include the following: 1.Identify the chromosome that influences business failure most through Genetic Algorithm’s strong searching capability. 2.Construct financial pre-warning models from Neural Network and traditional Discriminant Analysis techniques, and evaluate their pre-warning performance by comparing the ability to predict business failure three years before its occurrence. 3.Extract the reference value and the key descriptive indicator that influences business failure most through Decision Tree technique, thus enabling the investing public and associated authority to constantly monitor the key financial factors. The main characteristic of the Genetic Algorithm used in this study is its massive parallel optimizing ability. The analyses on the actual data show that: 1.Identify the Genetic component (chromosome) that influences business failure most through Genetic Algorithm: after 500 generations, the optimal chromosome combinations are Operating Income Ratio, Sales per Share, Earnings before Interest/Equity, Net Present Value per Stock (A), Net Present Value per Stock (B), Retained Profit Ratio, Cash Flow Adequacy Ratio, Times Interest Earned, Fixed Asset Turnover Ratio, and Operating Expense Ratio. 2.By employing the key indicators obtained from Genetic Algorithm, both Neural Network model and Discriminant Analysis model can accurately predict business failure (on average, for three-year ago prediction, hit ratio: 0.9500 compared with 0.9055). The hit ratios for both models are the same (0.9667) for one-year ago prediction. However, the hit ratios for two- and three-year ago predictions are higher for Neural Network model (0.9500 and 0.9333 compared with 0.9166 and 0.8333). This indicates that Neural Network pre-warning model has higher probability to successfully predict business failure earlier. 3.The Decision Tree cannot effectively distinguish the samples of successful and failure business. The following results are observed. When the Retained Profit Ratio of a business is larger than 0.9931, the business failure rate is about 88%. When the Retained Profit Ratio is larger than 0.9931 and Operating Income Ratio is lower than 0.0098, the business failure rate is as high as 97.33%.
Yang, Chia-Rong, and 楊佳榮. "Simulated Annealing, Genetic Algorithm, and Neural Network for Seismic Velocity Picking." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/09117340783882246780.
Повний текст джерела國立交通大學
生醫工程研究所
101
Velocity picking is an important step for seismic data processing. It is to pick several time-velocity pairs forming a polyline in a semblance image to represent the time and velocity relation in layers. Conventionally the geophysicists did it, but it took much time. We transfer it to a combinatorial problem which is finding the best combination from the set of candidate points. We define an objective function of energy that includes total semblance value of picked points, and constraints on the number of picked points, interval velocity, and velocity slope. We adopt three optimization methods: simulated annealing (SA), genetic algorithm (GA), and Hopfield neural network (HNN), to find the optimal solution of objective function and obtain the best polyline consisting of picked peak points respectively. In SA, the random system state represents a solution, and the higher energy random system state has a certain probability to be accepted and skip the local minimum. After annealing, the lowest energy system state is the best polyline. It is a global optimal solution. In GA, an individual represents a solution. Several individuals evolve many generations. The highest fitness individual in the last generation is the best polyline. It is also a global optimal solution. In HNN, the neurons of network represent a solution. We derive the equation of motion from the objective function and use asynchronous updating to renew the neurons of network. Finally, the network converges. The stable network state represents the best polyline. In GA, we find the maximum of the objective function. In SA and HNN, we find the minimum of the objective function. In the implementations of SA and HNN, we change the objective function to a negative function and find the minimum. In the parameter settings of SA and GA, we find the best parameter settings by sequential method. We have experiments on simulation data and Nankai real seismic data. We have 22 common midpoint gathers (CMP gathers) of simulated seismic data and 15 CMP gathers of Nankai real seismic data for experiments. We evaluate the performance by comparing the mean difference between the picking result of each adopted method and that of human. The experiments show that GA has the best result on the simulated and real seismic data experiments. The best picking results by three methods are further used to do normal move-out (NMO) correction and stacking. The results show that both of the signals of the simulated and real seismic data are enhanced. The results of velocity picking by three optimization methods will be helpful for further seismic data processing and interpretation.
Zhu, Yuqing. "Nonlinear system identification using a genetic algorithm and recurrent artificial neural networks." Thesis, 2006. http://spectrum.library.concordia.ca/9060/1/MR20771.pdf.
Повний текст джерелаCHIH, LIN CHIEN, and 林建智. "A study on integrated application of genetic algorithm and artificial neural network." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/26610585807673509540.
Повний текст джерелаWei, Chih-hsiu, and 韋至修. "Fuzzy Clustering by Distributed Genetic Algorithm and Multi-Synapse Neural Network Approaches." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/15901631622604488212.
Повний текст джерела國立臺灣科技大學
電機工程系
90
The area of research in this dissertation is fuzzy c-partition clustering, which is understood to be the grouping of similar objects with the concept of fuzzy set theory to incorporate the uncertainty of the final classification results. There are three parts in this dissertation. The first part is an overview of fuzzy c-partition clustering. In the second part, two distributed approaches of genetic search strategies for fuzzy clustering are proposed to surmount the problem of huge search space in the traditional combination of evolutionary algorithms and fuzzy c-partition clustering. The distributed optimization approaches proposed can divide the huge search space into many small ones, which in effect will lower the size of the total search space. The benefit of our approaches is especially shown in clusters with shell shapes, of which the basins of attraction of local minima are very small. In the third part, a new neural architecture, the multi-synapse neural network, is developed for constrained optimization problems, whose objective functions may include high order, logarithmic, sinusoidal forms, unlike the traditional Hopfield networks which can only handle quadratic form optimization. Meanwhile, based on the application of this new architecture, a fuzzy bidirectional associative clustering network (FBACN) is proposed for fuzzy c-partition clustering according to the objective-functional method. It is well known that fuzzy c-means is a milestone algorithm in the area of fuzzy c-partition clustering. All of the following objective-functional-based fuzzy c-partition algorithms incorporate the formulas of fuzzy c-means as the prime mover in their algorithms. However, when an application of fuzzy c-partition has sophisticated constraints, the necessity of analytical solutions in a single iteration step becomes a fatal issue of the existing algorithms. The largest advantage of FBACN is that it does not need analytical solutions. For the problems on which some prior information is known, we bring a concept of the combination of part crisp and part fuzzy clustering. Basically, the FBACN is composed of two layers of recurrent networks. Layer 1 can be a Hopfield network or a multi-synapse neural network based on whether its objective function is a quadratic form or a high order form. Yet layer 2 is definitely a multi-synapse neural network. Three examples are given in part III. The first two are the famous butterfly and Anderson’s Iris data sets, which are usually utilized as benchmarks. The last one is a data set with two concentric circles used to demonstrate the constrained fuzzy c-partition.
Lee, Ming-chang, and 李明璋. "Artificial Neural Network with Genetic Algorithm for Nonlinear Model of Machining Processes." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/88047810592371927245.
Повний текст джерела國立高雄第一科技大學
機械與自動化工程研究所
100
This study an artificial neural network (ANN) model with hybrid Taguchi-genetic algorithm (HTGA) is applied in a nonlinear multiple-input multiple-output (MIMO) model of machining processes. The HTGA in the MIMO ANN model optimizes parameters (i.e., weights of links and biases governing ) input-output relationships in the ANN by directly minimizing root-mean-squared error (RMSE), which is a key performance criterion. Experimental results show that, for nonlinear modeling of machining processes, the proposed MIMO HTGA-based ANN model has better prediction accuracy compared to conventional MIMO-based ANN models with backpropagation that are included in the Matlab toolbox.
Ho, Cheng-Yi, and 何承懌. "Optimal Chiller Loading by Genetic Algorithm based on Artificial Neural Network model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/d96phv.
Повний текст джерела國立臺北科技大學
能源與冷凍空調工程系碩士班
98
In large HVAC systems, the chiller is usually the most power-consuming component. Although different chillers have similar capacities and performance at the initial stage of operation, due to factors such as varying amount of water distribution, different installation locations, pump supply efficiency, chiller initiation sequence, operating time and so forth after specific amount of operation time, different chillers gradually exhibit varying levels of operational performance. In order to determine the characteristics of chiller operation, one must monitor the status of operation and temperature settings for chillers under different configurations while using relevant parameters to build the power consumption models for chillers. With sufficient understanding of the characteristics of various chiller operation in HVAC systems, it is possible to minimize power consumption by the system by keeping various chillers operating at optimal working conditions through chiller control whilst satisfying the required cooling load. In this research, the author has adopted a neural network and regression analysis to construct models of power consumption for chillers in various case studies in order to compare their R2 and average error. With the models completed, appropriate genetic algorithms were applied to compute the optimal load distribution; through the reproduction, crossover, mutation of the genetic algorithms and the coding/decoding of relevant parameters during the computation process, the author was able to derive the combination of the lowest power consumption for the chiller control (under the premise of satisfying the cooling load requirements). Results of the research revealed that chiller power consumption model constructed from neural network turned out to offer better accuracy compared to model constructed from regression analysis. Not only that, the chiller power consumption model constructed from the neural network also offered better number of converging generations and results in the computation of optimized load distribution using a genetic algorithm.
Lin, Yong-Cing, and 林永青. "A Study of Flight Time Using Artificial Neural Networks and Genetic Algorithm." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/v2d4ej.
Повний текст джерела國立臺北科技大學
工業工程與管理系所
93
Since the adoption of open-air policy by the government in 1987, people make more frequent use of air travel to do various business or tourism activities. The volume of air traffic has greatly increased, along with the occurrences of traffic jam in the air. Delays of landings or take-offs and the congestions in the approach air space have become commonplace, exacerbating the already heavy workload of air-traffic controllers and the inadequacies of ATC system. Therefore, a study of flight time in ATC operation to help alleviate airspace congestions has become more and more urgent and important. Taking international airway A1 as an example, this study makes use of the known entry time, flight altitude, speed, penetrating and descending as the input of artificial neural networks; the time between departure and transfer point as the output of Artificial Neural Networks, to establish artificial neural network. Applying artificial neural networks and genetic algorithm to the study to simulate the result of actual flight, one can precisely estimate the flight time, thereby making it an efficient air-traffic-control instrument. It can help controllers handle different time segments of air traffic, thus upgrading the quality of air traffic control service.
Chen, Yuan-Wen, and 陳淵文. "Using Neural Network and Genetic Algorithm to Implement Artificial Intelligence of Starcraft." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/31382797757704133886.
Повний текст джерела國立中正大學
資訊工程研究所
102
StarCraft is a Real-Time War Strategy video game developed by Blizzard Entertainment in 1998. Real Time Strategy Games are one of the most popular game schemes in PC markets and offer a dynamic environment that involves several interacting agents. The core strategies that need to be developed in these games are unit micro management, building order, resource management, and the game main tactic. The player must reason about high-level strategy and planning while having effective tactics. Unfortunately, current games only use scripted and fixed behaviors for their artificial intelligence, and the player can easily learn the counter measures to defeat the AI. Enabling an artificial agent to deal with such a task entails breaking down the complexity of this environment. In this paper, we describe a system based on neural networks that controls what units should do in the game StarCraft. The system combined with genetic algorithm which can learn better way to play this game.
Lu, Chin-Lung, and 呂金龍. "Integrating genetic algorithm and neural networks to predict the manufacturing process quality." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/23235695152575215410.
Повний текст джерела國立勤益科技大學
工業工程與管理系
100
The society demands change rapidly that consumers are attention to the product quality and require better product quality. The product quality is important to obtain the consumer’s favorite. It is result continue to improve by comparing quality and appearance in industry. The factories that is able to satisfy consumers will get more orders and earn more profit. The product is unable to satisfy consumers .The consumer market will disappear. Therefore, it is important to keep and improve the product quality. The steel ball is the basic materials of bearing industry. The Production patterns of steel ball are mass production and high product quality requirements. The manufacturing process of steel balls is: cold-pressing, light-grinding, heat treatment and fine grinding. Each assignment just produces a change, and then influence the balls quality. The steel balls are one of important parts of bearing which has different size, hardness and loads are installed in different types of bearings. In order to improve the efficiency of quality control. This study used the process capability analysis that the criteria of the steel balls classification in the manufacturing process. The number of steel balls process data and quality inspection data is used to classify and construction predict model. Applying the neural network constantly modify error and the genetic algorithm escape local solution to approximation of the optimal solution. In order to construct a prediction model of the steel balls process quality. The model is rapidly response to the process quality of each batch of steel balls to satisfy customers demand and obtain orders to enable promote the enterprise competition.
Huang, Hao-Fan, and 黃皓汎. "Clear Air Turbulence Avoidance Strategy via Genetic Algorithm & Neural Network Methods." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/54893682778818764720.
Повний текст джерела淡江大學
航空太空工程學系
90
In modern airline’s operation, clear air turbulence (CAT) remains one of the most influential factors in flight safety and flight quality consideration. In this research we use Matlab to create 3-D turbulence based on the real turbulence profiles, and prediction parameters (indices) T1, T2 and T3. The T1 factor is to define the turbulence intensity, the T2 and T3 factors are the response of aircraft in linear acceleration and three angular accelerations. Finally we use the genetic algorithm and combining the genetic algorithm (GA) and annealed neural network (ANN) methods to search the optimum escape trajectory. Results show moderate success that the computational time was shortened by 25% and with the same quality of solutions. It is hoped that the concepts and techniques implemented in this work could be used in future airborne Doppler radar research and flight simulation practice. In this work we first simulate turbulence/gust like three-dimensional wind profiles. The method is to use the Matlab tool and directly combine more then fifty trigonometric function waves. Comparing with real wind velocity profiles, the simulated wind show similar fluctuating behavior and can be used in our flight simulation. Secondly, to quantify the severity of CAT phenomenon, a set of prediction parameters(T1, T2, T3)have been proposed, T1 is three-dimensional turbulence acceleration, T2 is aircraft response in linear translation, and T3 is aircraft response in angular motion. These simulated T values show excellent agreement with real turbulence/gust T values. Thirdly, the classical rigid body, mass/mass distribution fixed flight dynamics equations are solved by standard 4th order Runge-Kutta method. To achieve an optimum flight trajectory in order to avoid the severity of CAT, two methods are employed as the steering tools, namely, the genetic algorithm and the genetic algorithm plus annealed neural network modification method. In our work the real-value GA approach is chosen due to its computation efficiency and similarity it the natural world. Our GA process is implemented as follow: both of T1+ T2+ T3, and T1+ T2+ T3+root mean squares of three Euler angles are assigned as the objective functions. And in the last, to further improve the computation efficiency of our work, the neural network method is added to our GA scheme. The model we selected is annealed neural network. It is relatively new and gives accurate data in a less timely fashion. Results show that this combination of GA and annealed neural network do improve the computation efficiency by 25%. When the CAT avoidance strategy is implemented and optimum flight trajectory achieved, it is obvious that direction attitude angles are also kept minimum. Thus represent a high degree of ride comfort and flight quality. It is hoped that the concepts proposed in this work will improve future passenger flight safety, and we no longer need to worry about clear air turbulence influence in our journey.