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Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Neural-genetic algorithm"

1

Cheng, Yu Gui. "Energy Demand Forecast of City Based on Cellular Genetic Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 2122–25. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2122.

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Анотація:
As a branch of genetic algorithm (GA), cellular genetic algorithm (CGA) has been used in search optimization of the population in recent years. Compared with traditional genetic algorithm and the algorithm combined with traditional genetic algorithm and BP neural network, energy demand forecast of city by the method of combining cellular genetic algorithm and BP neural network had the characteristic of the minimum training times, the shortest consumption time and the minimum error. Meanwhile, it was better than the other two algorithms from the point of fitting effect.
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2

Khamis, Azme Bin, and Phang Hou Yee. "A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price." European Journal of Engineering Research and Science 3, no. 6 (June 8, 2018): 10. http://dx.doi.org/10.24018/ejers.2018.3.6.758.

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Анотація:
The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed. Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.
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3

Khamis, Azme bin, and Phang Hou Yee. "A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price." European Journal of Engineering and Technology Research 3, no. 6 (June 8, 2018): 10–14. http://dx.doi.org/10.24018/ejeng.2018.3.6.758.

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Анотація:
The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed. Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.
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4

LEUNG, F. H. F., S. H. LING, and H. K. LAM. "AN IMPROVED GENETIC-ALGORITHM-BASED NEURAL-TUNED NEURAL NETWORK." International Journal of Computational Intelligence and Applications 07, no. 04 (December 2008): 469–92. http://dx.doi.org/10.1142/s1469026808002375.

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Анотація:
This paper presents a neural-tuned neural network (NTNN), which is trained by an improved genetic algorithm (GA). The NTNN consists of a common neural network and a modified neural network (MNN). In the MNN, a neuron model with two activation functions is introduced. An improved GA is proposed to train the parameters of the proposed network. A set of improved genetic operations are presented, which show superior performance over the traditional GA. The proposed network structure can increase the search space of the network and offer better performance than the traditional feed-forward neural network. Two application examples are given to illustrate the merits of the proposed network and the improved GA.
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5

Chen, Yin Ping, and Hong Xia Wu. "Fuzzy Neural Network Controller Based on Hybrid GA-BP Algorithm." Advanced Materials Research 823 (October 2013): 335–39. http://dx.doi.org/10.4028/www.scientific.net/amr.823.335.

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Анотація:
This paper presents a hybrid GA-BP algorithm for fuzzy neural network controller (FNNC). BP algorithm is a method to monitor learning, easily realized and with good local searching ability. But it depends too much on the the initial states of the network. Genetic algorithm is a random search algorithm which has strong global searching ability. The hybrid GA-BP algorithm ensure the global convergence of learning by genetic algorithm, overcomes the BP algorithms dependency on the initial states on the one hand. On the other hand, combined with the BP algorithm overcome the simple genetic algorithms randomness, improve the searching efficiency. The simulations on the inverted pendulun problem show good performance and robustness of the proposed fuzzy neural network controller based on hybrid GA-BP algorithm.
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6

Fakhri, Mansour, Ershad Amoosoltani, Mona Farhani, and Amin Ahmadi. "Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach." Canadian Journal of Civil Engineering 44, no. 11 (November 2017): 945–55. http://dx.doi.org/10.1139/cjce-2017-0124.

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Анотація:
The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.
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7

Wang, Hong Tao. "The Study on Neural Network Intelligent Method Based on Genetic Algorithm." Advanced Materials Research 271-273 (July 2011): 546–51. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.546.

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Анотація:
The paper gives the hybrid computational intelligence learning algorithm with global convergence, which is combined by BP algorithm and genetic algorithm. This algorithm connects the strengths of the BP algorithm and genetic algorithms. It not only has faster convergence, but also has a good global convergence property. The computer simulation results show that the hybrid algorithm is significantly better than the genetic algorithm and BP algorithm.
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8

Ke, Gang, and Ying Han Hong. "The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network." Applied Mechanics and Materials 599-601 (August 2014): 726–30. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.726.

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Анотація:
The traditional BP neural network algorithm is applied to intrusion detection system, detection speed slow and low detection accuracy. In order to solve the above problems, this paper proposes a network intrusion detection algorithm using genetic algorithms to optimize neural network weights. which find the most suitable weights of BP neural network by the genetic algorithm, and uses the optimized BP neural network to learn and detect the network intrusion detection data. Matlab simulation results show that the training sample time of the algorithm is shorter, has good intrusion recognition and detection effect, compared with the traditional network intrusion detection algorithm.
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9

Yan, Tai Shan. "Research on the Genetic Algorithm Simulating Human Reproduction Mode and its Blending Application with Neural Network." Advanced Materials Research 532-533 (June 2012): 1785–89. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1785.

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Анотація:
In this study, a genetic algorithm simulating human reproduction mode (HRGA) is proposed. The genetic operators of HRGA include selection operator, help operator, crossover operator and mutation operator. The sex feature, age feature and consanguinity feature of genetic individuals are considered. Two individuals with opposite sex can reproduce the next generation if they are distant consanguinity individuals and their age is allowable. Based on this genetic algorithm, an improved evolutionary neural network algorithm named HRGA-BP algorithm is formed. In HRGA-BP algorithm, HRGA is used firstly to evolve and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. Then, training samples are used to search for the optimal solution by the evolutionary neural network. HRGA-BP algorithm is used in motor fault diagnosis. The illustrational results show that HRGA-BP algorithm is better than traditional neural network algorithms in both speed and precision of convergence, and its validity in fault diagnosis is proved.
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10

Plawiak, Pawel, and Ryszard Tadeusiewicz. "Approximation of phenol concentration using novel hybrid computational intelligence methods." International Journal of Applied Mathematics and Computer Science 24, no. 1 (March 1, 2014): 165–81. http://dx.doi.org/10.2478/amcs-2014-0013.

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Анотація:
Abstract This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.
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Дисертації з теми "Neural-genetic algorithm"

1

Tong, Dong Ling. "Genetic algorithm-neural network : feature extraction for bioinformatics data." Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15788/.

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Анотація:
With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data.
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2

Blomström, 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.

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Анотація:
This thesis starts with a brief introduction to neural networks and the tuning of neural networks using genetic algorithms. An improved genetic algorithm is benchmarked using the technical paper Proben1 as a starting point. The benefits of using a genetic algorithm as well as results of the benchmark tests in comparison to a resilient backpropagation algorithm are discussed. The improved genetic algorithm is not a universal solution to all classification problems. Even though it outperforms  the resilient backpropagation algorithm slightly in these benchmark tests more benchmarking on more vast solution domains must be performed.
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3

Murnion, 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.

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4

McMurtrey, Shannon Dale. "Training and Optimizing Distributed Neural Networks Using a Genetic Algorithm." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/243.

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Анотація:
Parallelizing neural networks is an active area of research. Current approaches surround the parallelization of the widely used back-propagation (BP) algorithm, which has a large amount of communication overhead, making it less than ideal for parallelization. An algorithm that does not depend on the calculation of derivatives, and the backward propagation of errors, better lends itself to a parallel implementation. One well known training algorithm for neural networks explicitly incorporates network structure in the objective function to be minimized which yields simpler neural networks. Prior work has implemented this using a modified genetic algorithm in a serial fashion that is not scalable, thus limiting its usefulness. This dissertation created a parallel version of the algorithm. The performance of the proposed algorithm is compared against the existing algorithm using a variety of syn-thetic and real world problems. Computational experiments with benchmark datasets in-dicate that the parallel algorithm proposed in this research outperforms the serial version from prior research in finding better minima in the same time as well as identifying a simpler architecture.
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5

Reiling, Anthony J. "Convolutional Neural Network Optimization Using Genetic Algorithms." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.

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6

Kopel, Ariel. "NEURAL NETWORKS PERFORMANCE AND STRUCTURE OPTIMIZATION USING GENETIC ALGORITHMS." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/840.

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Анотація:
Artificial Neural networks have found many applications in various fields such as function approximation, time-series prediction, and adaptive control. The performance of a neural network depends on many factors, including the network structure, the selection of activation functions, the learning rate of the training algorithm, and initial synaptic weight values, etc. Genetic algorithms are inspired by Charles Darwin’s theory of natural selection (“survival of the fittest”). They are heuristic search techniques that are based on aspects of natural evolution, such as inheritance, mutation, selection, and crossover. This research utilizes a genetic algorithm to optimize multi-layer feedforward neural network performance and structure. The goal is to minimize both the function of output errors and the number of connections of network. The algorithm is modeled in C++ and tested on several different data sets. Computer simulation results show that the proposed algorithm can successfully determine the appropriate network size for optimal performance. This research also includes studies of the effects of population size, crossover type, probability of bit mutation, and the error scaling factor.
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7

MacLeod, Christopher. "The synthesis of artificial neural networks using single string evolutionary techniques." Thesis, Robert Gordon University, 1999. http://hdl.handle.net/10059/367.

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Анотація:
The research presented in this thesis is concerned with optimising the structure of Artificial Neural Networks. These techniques are based on computer modelling of biological evolution or foetal development. They are known as Evolutionary, Genetic or Embryological methods. Specifically, Embryological techniques are used to grow Artificial Neural Network topologies. The Embryological Algorithm is an alternative to the popular Genetic Algorithm, which is widely used to achieve similar results. The algorithm grows in the sense that the network structure is added to incrementally and thus changes from a simple form to a more complex form. This is unlike the Genetic Algorithm, which causes the structure of the network to evolve in an unstructured or random way. The thesis outlines the following original work: The operation of the Embryological Algorithm is described and compared with the Genetic Algorithm. The results of an exhaustive literature search in the subject area are reported. The growth strategies which may be used to evolve Artificial Neural Network structure are listed. These growth strategies are integrated into an algorithm for network growth. Experimental results obtained from using such a system are described and there is a discussion of the applications of the approach. Consideration is given of the advantages and disadvantages of this technique and suggestions are made for future work in the area. A new learning algorithm based on Taguchi methods is also described. The report concludes that the method of incremental growth is a useful and powerful technique for defining neural network structures and is more efficient than its alternatives. Recommendations are also made with regard to the types of network to which this approach is best suited. Finally, the report contains a discussion of two important aspects of Genetic or Evolutionary techniques related to the above. These are Modular networks (and their synthesis) and the functionality of the network itself.
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8

Deane, 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.

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9

Fischer, 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.

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Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness. (authors' abstract)
Series: Discussion Papers of the Institute for Economic Geography and GIScience
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10

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.

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This research tests the ability of artificial learning methodologies to map market returns better than logistic regression. The learning methodologies used are neural networks and dual-layered genetic algorithms. These methodologies are used to develop a trading strategy to generate excess returns. The excess returns are compared to test the trading strategy's effectiveness. Market-adjusted and size-adjusted excess returns are calculated. Using a trading strategy based approach the logistic regression models generated greater returns than the neural network and dual-layered genetic algorithm models. It appears that the noise in the financial markets prevents the artificial learning methodologies from properly mapping the market returns. The results confirm the findings that fundamental analysis can be used to generate excess returns.
Ph. D.
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Книги з теми "Neural-genetic algorithm"

1

Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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2

Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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3

Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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4

Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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5

Vas, Peter. Artificial-intelligence-based electrical machines and drives: Application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques. Oxford: Oxford University Press, 1999.

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6

C, Jain L., and Johnson R. P, eds. Neural network training using genetic algorithms. Singapore: World Scientific, 1996.

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7

Albrecht, Rudolf F., Colin R. Reeves, and Nigel C. Steele, eds. Artificial Neural Nets and Genetic Algorithms. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0.

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8

Pearson, David W., Nigel C. Steele, and Rudolf F. Albrecht. Artificial Neural Nets and Genetic Algorithms. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4.

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9

Dobnikar, Andrej, Nigel C. Steele, David W. Pearson, and Rudolf F. Albrecht. Artificial Neural Nets and Genetic Algorithms. Vienna: Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-6384-9.

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10

Kůrková, Věra, Roman Neruda, Miroslav Kárný, and Nigel C. Steele, eds. Artificial Neural Nets and Genetic Algorithms. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9.

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Частини книг з теми "Neural-genetic algorithm"

1

Alander, J. T., T. Mantere, and P. Turunen. "Genetic Algorithm Based Software Testing." In Artificial Neural Nets and Genetic Algorithms, 325–28. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_71.

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2

Uesaka, Yoshinori. "Convergence of Algorithm and the Schema Theorem in Genetic Algorithms." In Artificial Neural Nets and Genetic Algorithms, 210–13. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_56.

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3

Kussul, Ernst M., and Tatyana N. Baidyk. "Genetic Algorithm for Neurocomputer Image Recognition." In Artificial Neural Nets and Genetic Algorithms, 120–23. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_33.

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4

Gröbner, Matthias, and Peter Wilke. "Rostering with a Hybrid Genetic Algorithm." In Artificial Neural Nets and Genetic Algorithms, 316–19. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9_78.

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5

Beiu, Valeriu, and John G. Taylor. "VLSI Optimal Neural Network Learning Algorithm." In Artificial Neural Nets and Genetic Algorithms, 61–64. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_18.

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6

Oppacher, F., and M. Wineberg. "A Canonical Genetic Algorithm Based Approach to Genetic Programming." In Artificial Neural Nets and Genetic Algorithms, 401–4. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_88.

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7

Nagy, Ivan, Petr Nedoma, and Miroslav Kárný. "Factorized EM Algorithm for Mixture Estimation." In Artificial Neural Nets and Genetic Algorithms, 402–5. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9_100.

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Güvenir, H. Altay. "A Genetic Algorithm for Multicriteria Inventory Classification." In Artificial Neural Nets and Genetic Algorithms, 6–9. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_4.

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9

Haas, O. C. L., K. J. Burnham, M. H. Fisher, and J. A. Mills. "Genetic Algorithm Applied to Radiotherapy Treatment Planning." In Artificial Neural Nets and Genetic Algorithms, 432–35. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_112.

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10

Alander, Jarmo T. "On Robot Navigation Using a Genetic Algorithm." In Artificial Neural Nets and Genetic Algorithms, 471–78. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0_68.

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Тези доповідей конференцій з теми "Neural-genetic algorithm"

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Setyawati, Bina R., Robert C. Creese, and Sidharta Sahirman. "Genetic algorithm for neural networks optimization." In Optics East, edited by Bhaskaran Gopalakrishnan. SPIE, 2004. http://dx.doi.org/10.1117/12.578064.

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Kajan, Slavomir, and Stefan Kozak. "Neural-genetic control algorithm of robots." In 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). IEEE, 2014. http://dx.doi.org/10.1109/raad.2014.7002243.

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ROHAYANI, Hetty, Tuga MAURITSIUS, Leslie H. Spit Warnars Harco, and Edi ABDURRACHMAN. "Evaluation Performance Neural Network Genetic Algorithm." In Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aisr.k.200424.065.

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Tan, Xiao. "LIBOR Prediction Using Genetic Algorithm and Genetic Algorithm Integrated with Recurrent Neural Network." In 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019. http://dx.doi.org/10.1109/gcat47503.2019.8978299.

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Idrissi, Mohammed Amine Janati, Hassan Ramchoun, Youssef Ghanou, and Mohamed Ettaouil. "Genetic algorithm for neural network architecture optimization." In 2016 3rd International Conference on Logistics Operations Management (GOL). IEEE, 2016. http://dx.doi.org/10.1109/gol.2016.7731699.

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Nezamoddini, Nasim, and Amirhosein Gholami. "Integrated Genetic Algorithm and Artificial Neural Network." In 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2019. http://dx.doi.org/10.1109/cse/euc.2019.00057.

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Uang, Chii-Maw, Wen-Gong Chen, and Ji-Bin Horng. "Genetic-algorithm-based tri-state neural networks." In Photonics Asia 2002, edited by Guoguang Mu, Francis T. S. Yu, and Suganda Jutamulia. SPIE, 2002. http://dx.doi.org/10.1117/12.483202.

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Sampath, Suresh, and Riti Singh. "An Integrated Fault Diagnostics Model Using Genetic Algorithm and Neural Networks." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53914.

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This paper presents the development of an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using Genetic Algorithm and Artificial Neural Network. The diagnostics model operates in two distinct stages. The first stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses concept of a hybrid diagnostics model in which a nested neural network is used with genetic algorithm to form a hybrid diagnostics model. The nested neural network functions as a pre-processor or filter to reduce the number of fault classes to be explored by the genetic algorithm based diagnostics model. The hybrid model improves the accuracy, reliability and consistency of the results obtained. In addition significant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR21 engine has been used as the test engine for implementing the diagnostics model.
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Chaiyaratana, Nachol, and Ali M. S. Zalzala. "Time-Optimal Path Planning and Control Using Neural Networks and a Genetic Algorithm." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/dsc-24512.

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Abstract This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-dof robotic system. Extended Kohonen networks which contain an additional lattice of output neurons are used in conjunction with PID controllers in position control to minimise command tracking errors. The results indicate that the extended Kohonen network controller is more efficient than the trajectory preshaping scheme reported in early literature. Subsequently, a multi-objective genetic algorithm (MOGA) is used to solve an optimisation problem related to time-optimal control. This problem involves the selection of actuator torque limits and an end-effector path subject to time-optimality and tracking error constraints. Two chromosome coding schemes are explored in the investigation: Gray and integer-based coding schemes. The results suggest that the integer-based chromosome is more suitable at representing the decision variables. As a result of using both neural networks and a genetic algorithm in this application, an idea of a hybridisation between a neural network and a genetic algorithm at the task level for use in a control system is also effectively demonstrated.
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Jinhee Lee, Se-Young Oh, Chintae Choi, and Heedon Jeong. "Flow estimation using genetic algorithm and neural network." In 2009 IEEE International Conference on Industrial Technology - (ICIT). IEEE, 2009. http://dx.doi.org/10.1109/icit.2009.4939634.

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Звіти організацій з теми "Neural-genetic algorithm"

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Rogers, Leah L. Optimal groundwater remediation using artificial neural networks and the genetic algorithm. Office of Scientific and Technical Information (OSTI), August 1992. http://dx.doi.org/10.2172/10102700.

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Guo, Boyun, Andrew Duguid, and Ronar Nygaard. Statistical Analysis of CO2 Exposed Wells to Predict Long Term Leakage through the Development of an Integrated Neural-Genetic Algorithm. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1373948.

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Sharp, David H., John Reinitz, and Eric Mjolsness. Genetic Algorithms for Genetic Neural Nets. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada256223.

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Sullivan, Jr, Lai John M., and Q. Application of Neural Networks Coupled with Genetic Algorithms to Optimize Soil Cleanup Operations in Cold Climates. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada637453.

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