Thematische Bibliographien / Evolutionary development of neural network

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Evolutionary development of neural network“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 5. Februar 2022

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Zeitschriftenartikel zum Thema "Evolutionary development of neural network"

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Al-Khowarizmi, Al-Khowarizmi. „Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network“. JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, Nr. 2 (20.01.2020): 258–65. http://dx.doi.org/10.31289/jite.v3i2.3284.

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Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better
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Li, Xiao Guang. „Research on the Development and Applications of Artificial Neural Networks“. Applied Mechanics and Materials 556-562 (Mai 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.

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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.
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Xue, Yu, Pengcheng Jiang, Ferrante Neri und Jiayu Liang. „A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks“. International Journal of Neural Systems 31, Nr. 09 (24.07.2021): 2150035. http://dx.doi.org/10.1142/s0129065721500350.

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With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.
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Odri, Stevan V., Dusan P. Petrovacki und Gordana A. Krstonosic. „Evolutional development of a multilevel neural network“. Neural Networks 6, Nr. 4 (Januar 1993): 583–95. http://dx.doi.org/10.1016/s0893-6080(05)80061-9.

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LI, KANG, und JIAN-XUN PENG. „SYSTEM ORIENTED NEURAL NETWORKS — PROBLEM FORMULATION, METHODOLOGY AND APPLICATION“. International Journal of Pattern Recognition and Artificial Intelligence 20, Nr. 02 (März 2006): 143–58. http://dx.doi.org/10.1142/s0218001406004570.

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A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful "white box" neural network model with better generalization performance. In this paper, the problem formulation, the neural network configuration, and the associated optimization software are discussed in detail. This methodology is then applied to a practical real-world system to illustrate its effectiveness.
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Wu, Tao, Jiao Shi, Deyun Zhou, Xiaolong Zheng und Na Li. „Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network“. Sensors 21, Nr. 17 (02.09.2021): 5901. http://dx.doi.org/10.3390/s21175901.

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Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot pruning framework only needs to perform filter pruning and model fine-tuning once. Moreover, we built a constraint multi-objective filter pruning problem in which two objectives represent the filter pruning ratio and the accuracy of the pruned convolutional neural network, respectively. A non-dominated sorting-based evolutionary multi-objective algorithm was used to solve the filter pruning problem, and it provides a set of Pareto solutions which consists of a series of different trade-off pruned models. Finally, some models are uniformly selected from the set of Pareto solutions to be fine-tuned as the output of our method. The effectiveness of our method was demonstrated in experimental studies on four designed models, LeNet and AlexNet. Our method can prune over 85%, 82%, 75%, 65%, 91% and 68% filters with little accuracy loss on four designed models, LeNet and AlexNet, respectively.
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Debeljak, Željko, Viktor Marohnić, Goran Srečnik und Marica Medić-Šarić. „Novel approach to evolutionary neural network based descriptor selection and QSAR model development“. Journal of Computer-Aided Molecular Design 19, Nr. 12 (11.04.2006): 835–55. http://dx.doi.org/10.1007/s10822-005-9022-2.

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Jung, Sung Young. „A Topographical Method for the Development of Neural Networks for Artificial Brain Evolution“. Artificial Life 11, Nr. 3 (Juni 2005): 293–316. http://dx.doi.org/10.1162/1064546054407185.

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Developmental neural networks, which are constructed according to developmental rules (i.e., genes), have the potential to be differentiated into heteromorphic neural structures capable of performing various kinds of activities. The fact that the biological neural architectures are found to be highly repetitive, layered, and topographically organized has important consequences for neural development methods. The purpose of this article is to propose a neural development method that can construct topographical neural connections, that is, a topographical development method, to facilitate fast and efficient development. This is achieved by arborizing neural connections on a developmental tree that rarely produces dead connections. Modular gene expression and corresponding modular networks have an important role in a gradual evolutionary process. Gene expression for modular networks is also proposed here as a way to reduce the probability of fatal mutants created through gene alteration. The corresponding evolutionary experiment shows that various neural structures—layered, repetitive, modular, and complex ones like those in the biological brain—can be constructed and easily observed. It also demonstrates that due to the efficiency of the proposed method, large neural networks can be easily managed, thereby making it suitable for long duration evolutionary experiments.
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Bury, Y. A., und D. I. Samal. „APPLICATION OF THE EVOLUTIONARY PARADIGM TO DESIGNING ARCHITEСTURE OF A NEURAL NETWORK FOR RECOGNIZING THE DISTORTED TEXT“. «System analysis and applied information science», Nr. 4 (08.02.2018): 45–50. http://dx.doi.org/10.21122/2309-4923-2017-4-45-50.

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The paper presents an attempt to apply of evolutionary methods to the design and training of a system for recognizing distorted text.Over the past decades, artificial neural networks are widely used in many areas of artificial intelligence, such as forecasting, optimization, data analysis, pattern recognition and decision making. Nevertheless, the traditional heuristic approaches to design of multi-layer neural networks are based on the recombination of already existing neural network architectures.This approach allows us to solve a wide range of problems, but implies compliance with specific conditions for the quality work of algorithms.The natural analogues of such intelligent systems in living nature, however, are universal enough to adapt to virtually any habitat.Despite their extreme complexity and limited ability to study their structures, it is known that these structures were formed as a result of the evolutionary process. And if today it is impossible to determine the exact architecture of the links in biological neural systems, then at least one can try to reproduce the very process of their formation in order to obtain a more universal algorithm than those developed to the present moment.In opposite to them we form the final structure of the core of the classification system by evolutionary process, taking into account the knowledge about the features of the development and construction of the nervous system of vertebrates.Applying of the approach makes it possible to abstract from the limitations of existing neural network algorithms, caused by the scope of application of specific types of their structures.
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Khan, Gul Muhammad, Julian F. Miller und David M. Halliday. „Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture“. Evolutionary Computation 19, Nr. 3 (September 2011): 469–523. http://dx.doi.org/10.1162/evco_a_00043.

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Although artificial neural networks have taken their inspiration from natural neurological systems, they have largely ignored the genetic basis of neural functions. Indeed, evolutionary approaches have mainly assumed that neural learning is associated with the adjustment of synaptic weights. The goal of this paper is to use evolutionary approaches to find suitable computational functions that are analogous to natural sub-components of biological neurons and demonstrate that intelligent behavior can be produced as a result of this additional biological plausibility. Our model allows neurons, dendrites, and axon branches to grow or die so that synaptic morphology can change and affect information processing while solving a computational problem. The compartmental model of a neuron consists of a collection of seven chromosomes encoding distinct computational functions inside the neuron. Since the equivalent computational functions of neural components are very complex and in some cases unknown, we have used a form of genetic programming known as Cartesian genetic programming (CGP) to obtain these functions. We start with a small random network of soma, dendrites, and neurites that develops during problem solving by repeatedly executing the seven chromosomal programs that have been found by evolution. We have evaluated the learning potential of this system in the context of a well-known single agent learning problem, known as Wumpus World. We also examined the harder problem of learning in a competitive environment for two antagonistic agents, in which both agents are controlled by independent CGP computational networks (CGPCN). Our results show that the agents exhibit interesting learning capabilities.
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Dissertationen zum Thema "Evolutionary development of neural network"

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Bush, Brian O. „Development of a fuzzy system design strategy using evolutionary computation“. Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.

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Townsend, Joseph Paul. „Artificial development of neural-symbolic networks“. Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbolic Integration [4, 16, 21, 40], in which ANNs are used to produce interpretable, adaptable representations of logic programs and other symbolic models. One neural-symbolic model of reasoning is SHRUTI [89, 95], argued to exhibit biological plausibility in that it captures some aspects of real biological processes. SHRUTI's original developers also suggest that further biological plausibility can be ascribed to the fact that SHRUTI networks can be represented by a model of genetic development [96, 120]. The aims of this thesis are to support the claims of SHRUTI's developers by producing the first such genetic representation for SHRUTI networks and to explore biological plausibility further by investigating the evolvability of the proposed SHRUTI genome. The SHRUTI genome is developed and evolved using principles from Generative and Developmental Systems and Artificial Development [13, 105], in which genomes use indirect encoding to provide a set of instructions for the gradual development of the phenotype just as DNA does for biological organisms. This thesis presents genomes that develop SHRUTI representations of logical relations and episodic facts so that they are able to correctly answer questions on the knowledge they represent. The evolvability of the SHRUTI genomes is limited in that an evolutionary search was able to discover genomes for simple relational structures that did not include conjunction, but could not discover structures that enabled conjunctive relations or episodic facts to be learned. Experiments were performed to understand the SHRUTI fitness landscape and demonstrated that this landscape is unsuitable for navigation using an evolutionary search. Complex SHRUTI structures require that necessary substructures must be discovered in unison and not individually in order to yield a positive change in objective fitness that informs the evolutionary search of their discovery. The requirement for multiple substructures to be in place before fitness can be improved is probably owed to the localist representation of concepts and relations in SHRUTI. Therefore this thesis concludes by making a case for switching to more distributed representations as a possible means of improving evolvability in the future.
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Hytychová, Tereza. „Evoluční návrh neuronových sítí využívající generativní kódování“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445478.

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The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datasets. The method is also able to develop neural networks capable of solving multiple problems at once while slightly reducing accuracy.
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Kadiyala, Akhil. „Development and Evaluation of an Integrated Approach to Study In-Bus Exposure Using Data Mining and Artificial Intelligence Methods“. University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341257080.

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Adams, Bryan (Bryan Paul) 1977. „Evolutionary, developmental neural networks for robust robotic control“. Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37900.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 136-143).
The use of artificial evolution to synthesize controllers for physical robots is still in its infancy. Most applications are on very simple robots in artificial environments, and even these examples struggle to span the "reality gap," a name given to the difference between the performance of a simulated robot and the performance of a.real robot using the same evolved controller. This dissertation describes three methods for improving the use of artificial evolution as a tool for generating controllers for physical robots. First, the evolutionary process must incorporate testing on the physical robot. Second, repeated structure on the robot should be exploited. Finally, prior knowledge about the robot and task should be meaningfully incorporated. The impact of these three methods, both in simulation and on physical robots, is demonstrated, quantified, and compared to hand-designed controllers.
by Bryan Adams.
Ph.D.
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Tsui, Kwok Ching. „Neural network design using evolutionary computing“. Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299918.

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Hayward, Serge. „Financial forecasting and modelling with an evolutionary artificial neural network“. Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439394.

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Hlynka, Markian D. „A framework for an automated neural network designer using evolutionary algorithms“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0014/MQ41716.pdf.

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Jagadeesan, Ananda Prasanna. „Real time evolutionary algorithms in robotic neural control systems“. Thesis, Robert Gordon University, 2006. http://hdl.handle.net/10059/436.

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This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is “off-line,” as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated Legged Robot as a test-bed and allows it to adapt to a changing Fitness function. An example of this in reality would be a robot walking from a solid surface onto an unknown surface (which might be, for example, rock or sand) while optimising its controlling network in real-time, to adjust its locomotive gait, accordingly. The project initially developed a Central Pattern Generator (CPG) for a Bipedal Robot and used this to explore the basic characteristics of RTEA. The system was then developed to operate on a Quadruped Robot and a test regime set up which provided thousands of real-environment like situations to test the RTEA’s ability to control the robot. The programming for the system was done using Borland C++ Builder and no commercial simulation software was used. Through this means, the Evolutionary Operators of the RTEA were examined and their real-time performance evaluated. The results demonstrate that a RTEA can be used successfully to optimise an ANN in real-time. They also show the importance of Neural Functionality and Network Topology in such systems and new models of both neurons and networks were developed as part of the project. Finally, recommendations for a working system are given and other applications reviewed.
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Jakobsson, Henrik. „Inversion of an Artificial Neural Network Mapping by Evolutionary Algorithms with Sharing“. Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-165.

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Inversion of the artificial neural network mapping is a relatively unexplored field of science. By inversion we mean that a search is conducted to find what input patterns that corresponds to a specific output pattern according to the analysed network. In this report, an evolutionary algorithm is proposed to conduct the search for input patterns. The hypothesis is that the inversion with the evolutionary search-method will result in multiple, separate and equivalent input patterns and not get stuck in local optima which possibly would cause the inversion to result in erroneous answer. Beside proving the hypothesis, the tests are also aimed at explaining the nature of inversion and how the result of inversion should be interpreted. At the end of the document a long list of proposed future work is suggested. Work, which might result in a deeper understanding of what the inversion means and maybe an automated analysis tool, based on inversion.

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Bücher zum Thema "Evolutionary development of neural network"

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Lou, Padgett Mary, Lindblad Thomas, Society for Computer Simulation und United States. National Aeronautics and Space Administration., Hrsg. Sixth, Seventh, and Eighth Workshops on Virtual Intelligence: Academic/Industrial/NASA/Defense: Technical interchange and tutorials : International Conferences on Virtual Intelligence, Fuzzy Systems, Evolutionary Computing, and Virtual Reality 1996. Bellingham, Wash: SPIE, 1996.

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C, Jain L., und Johnson R. P, Hrsg. Automatic generation of neural network architecture using evolutionary computation. Singapore: World Scientific, 1997.

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Jorgensen, Charles C. Development of a sensor coordinated kinematic model for neural network controller training. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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International, Symposium on Computational Intelligence and Design (1st 2008 Wuhan China). Proceedings of the 2008 International Symposium on Computational Intelligence and Design: October 17-18, 2008, Wuhan, China. Los Alamitos, Calif: IEEE Computer Society, 2008.

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International Symposium on Computational Intelligence and Design (2nd 2009 Changsha, China). Proceedings: 2009 International Symposium on Computational Intelligence and Design : Changsha, China, 12-14 December 2009. Los Alamitos, Calif: IEEE Computer Society, 2008.

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International Symposium on Computational Intelligence and Design (3rd 2010 Hangzhou, Zhejiang, China). Proceedings: 2010 International Symposium on Computational Intelligence and Design : ICSID 2010 : 29-31 October 2010, Hangzhou, Zhejiang, China. Los Alamitos, Calif: IEEE Computer Society, 2010.

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International Conference on Innovative Computing, Information and Control (1st 2006 Beijing, China). ICICIC 2006: First International Conference on Innovative Computing, Information and Control : 30 August-1 September, 2006, Beijing, China. Herausgegeben von Pan Jeng-Shyang, Shi Peng 1958-, Zhao Yao und Institute of Electrical and Electronics Engineers. Los Alamitos, Calif: IEEE Computer Society, 2006.

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Topping, B. H. Developments in Neural Networks and Evolutionary Computing for Civil and Structural Engineering. Hyperion Books, 1995.

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Semi-Empirical Neural Network Modeling and Digital Twins Development. Elsevier, 2020. http://dx.doi.org/10.1016/c2017-0-02027-x.

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Software Development Outsourcing Decision Support Tool with Neural Network Learning. Storming Media, 2004.

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Buchteile zum Thema "Evolutionary development of neural network"

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Cho, Sung-Bae, und Katsunori Shimohara. „Grammatical Development of Evolutionary Modular Neural Networks“. In Lecture Notes in Computer Science, 413–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48873-1_53.

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Shakya, S., M. Kern, G. Owusu und C. M. Chin. „Dynamic Pricing with Neural Network Demand Models and Evolutionary Algorithms“. In Research and Development in Intelligent Systems XXVII, 223–36. London: Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-130-1_16.

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Manuputty, J., P. Sen und D. Todd. „Development of an Iterative Neural Network and Genetic Algorithm Procedure for Shipyard Scheduling“. In Evolutionary Design and Manufacture, 335–42. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0519-0_27.

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Shailaja, M., und A. V. Sita Rama Raju. „Development of Back Propagation Neural Network (BPNN) Model to Predict Combustion Parameters of Diesel Engine“. In Swarm, Evolutionary, and Memetic Computing, 71–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48959-9_7.

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Barrios, D., A. Carrascal, D. Manrique und J. Rios. „ADANNET: Automatic Design of Artificial Neural Networks by Evolutionary Techniques“. In Research and Development in Intelligent Systems XVIII, 67–80. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0119-2_6.

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Dong, Xueshi, Wenyong Dong, Yunfei Yi, Yajie Wang und Xiaosong Xu. „The Recent Developments and Comparative Analysis of Neural Network and Evolutionary Algorithms for Solving Symbolic Regression“. In Intelligent Computing Theories and Methodologies, 703–14. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22180-9_70.

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Rocha, Miguel, Paulo Cortez und José Neves. „Evolutionary Neural Network Learning“. In Progress in Artificial Intelligence, 24–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24580-3_10.

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Mat Noor, R. A. „Recent Developments of Neural Networks in Biodiesel Applications“. In Swarm, Evolutionary, and Memetic Computing, 339–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_30.

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Khan, Gul Muhammad. „Evolutionary Computation“. In Evolution of Artificial Neural Development, 29–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67466-7_3.

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Croll, Roger P. „Neural Development in Invertebrates“. In The Wiley Handbook of Evolutionary Neuroscience, 307–49. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118316757.ch11.

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Konferenzberichte zum Thema "Evolutionary development of neural network"

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Roy, Anthony M., Erik K. Antonsson und Andrew A. Shapiro. „Genetic Evolution for the Development of Robust Artificial Neural Network Logic Gates“. In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87448.

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Control tasks involving dramatic non-linearities, such as decision making, can be challenging for classical design methods. However, autonomous stochastic design methods have proved effective. In particular, Genetic Algorithms (GA) that create phenotypes by the application of genotypes comprising rules are robust and highly scalable. Such encodings are useful for complex applications such as artificial neural net design. This paper outlines an evolutionary algorithm that creates C++ programs which in turn create Artificial Neural Networks (ANNs) that can functionally perform as an exclusive-OR logic gate. Furthermore, the GAs are able to create scalable ANNs robust enough to feature redundancies that allow the network to function despite internal failures.
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Miller, Julian F., und Dennis G. Wilson. „A developmental artificial neural network model for solving multiple problems“. In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3075976.

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Huan, Tran Thien, Cao Van Kien und Ho Pham Huy Anh. „Adaptive Evolutionary Neural Network Gait Generation for Humanoid Robot Optimized with Modified Differential Evolution Algorithm“. In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD). IEEE, 2018. http://dx.doi.org/10.1109/gtsd.2018.8595586.

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LA PAZ-MARÍN, MÓNICA DE, PILAR CAMPOY-MUÑOZ und CÉSAR HERVÁS-MARTÍNEZ. „EVOLUTIONARY NEURAL NETWORK CLASSIFIERS FOR MONITORING RESEARCH, DEVELOPMENT AND INNOVATION PERFORMANCE IN EUROPEAN UNION MEMBER STATES“. In Proceedings of the XVII SIGEF Congress. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814415774_0021.

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Plyakin, Vladislav, und Vladislav Protasov. „Evolutionary matching method for face recognition using neural networks“. In International Conference "Computing for Physics and Technology - CPT2020". ANO «Scientific and Research Center for Information in Physics and Technique», 2020. http://dx.doi.org/10.30987/conferencearticle_5fd755bf868b47.13424079.

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The problem of formalizing and automating the process of recognizing human faces was touched upon at the earliest stages of the development of image recognition systems and remains relevant to this day. Moreover, over the past ten years, the number of scientific studies and publications on this topic has increased several times, which indicates an increase in the urgency of this problem. This can be explained by the fact that modern computing technology opens up new possibilities for its application in various fields, and, accordingly, a lot of applied problems have appeared that require their speedy resolution. One of the practical applications of the pattern recognition theory is face recognition, the task of which is to automatically localize a face in an image and identify a person by face. The interest in the procedures underlying the process of localization and face recognition is quite significant due to the variety of their practical applications in areas such as security systems, verification, forensic examination, teleconferences, computer games, etc. For example, the face recognition system developed at Beijing Tsinghua University has been certified by the Chinese Ministry of Public Security for use in public places. Omron Japan, which specializes in recognition, automation and control technologies, has developed a human face recognition system for mobile phones. Riya, founded by a group of specialists in facial recognition algorithms from Stanford University, has begun open testing of a Web service for contextual search of facial images in digital photo albums. The abundance of such examples indicates the practical importance and relevance of face recognition methods.
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Katragadda, Ravi Teja, Sreekanth Reddy Gondipalle, Paolo Guarneri und Georges Fadel. „Predicting the Thermal Performance for the Multi-Objective Vehicle Underhood Packing Optimization Problem“. In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71098.

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The ever increasing demands towards improvement in vehicle performance and passenger comfort have led the automotive manufacturers to further enhance the design in the early stages of the vehicle development process. Though, these design changes enhance the overall vehicle performance to an extent, the placement of these components under the car hood also plays a vital role in increasing the vehicle performance. In the past, a study on the automobile underhood packaging or layout problem was conducted and a multi-objective optimization routine with three objectives namely, minimizing center of gravity height, maximizing vehicle components accessibility and maximizing survivability (for army vehicles) has been setup to determine the optimal locations of the underhood components. The previous study did not consider thermal performance as an objective. This study asserts the necessity of including thermal performance as an objective and makes an assessment of the several available thermal analyses that are performed on the automotive underhood to evaluate the thermal objective. A Neural Network approximation of the CFD analysis conducted over the automotive underhood is presented in this paper. The results obtained from the Neural Network are compared with the CFD results, showing good agreement. The Neural Network model is included in the multi-objective optimization routine and new layout results are obtained. A non-deterministic evolutionary multi-objective algorithm (AMGA-2) is used to perform the optimization process.
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Weatheritt, Jack, Richard D. Sandberg, Julia Ling, Gonzalo Saez und Julien Bodart. „A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow“. In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63403.

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Classical RANS turbulence models have known deficiencies when applied to jets in crossflow. Identifying the linear Boussinesq stress-strain hypothesis as a major contribution to erroneous prediction, we consider and contrast two machine learning frameworks for turbulence model development. Gene Expression Programming, an evolutionary algorithm that employs a survival of the fittest analogy, and a Deep Neural Network, based on neurological processing, add non-linear terms to the stress-strain relationship. The results are Explicit Algebraic Stress Model-like closures. High fidelity data from an inline jet in crossflow study is used to regress new closures. These models are then tested on a skewed jet to ascertain their predictive efficacy. For both methodologies, a vast improvement over the linear relationship is observed.
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Ivan, Zelinka, Senkerik Roman und Oplatkova Zuzana. „Evolutionary Scanning and Neural Network Optimization“. In 2008 19th International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2008. http://dx.doi.org/10.1109/dexa.2008.84.

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„Evolutionary Techniques for Neural Network Optimization“. In The First International Workshop on Artificial Neural Networks and Intelligent Information Processing. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001191800030011.

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Saiki, Motohiro, und Satoshi Matsuda. „Evolutionary neural network model of universal grammar“. In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596735.

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Berichte der Organisationen zum Thema "Evolutionary development of neural network"

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McDonnell, J. R., W. C. Page und D. E. Waagen. Neural Network Construction Using Evolutionary Search. Fort Belvoir, VA: Defense Technical Information Center, Dezember 1994. http://dx.doi.org/10.21236/ada290862.

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Matteucci, Matteo. ELeaRNT: Evolutionary Learning of Rich Neural Network Topologies. Fort Belvoir, VA: Defense Technical Information Center, Januar 2006. http://dx.doi.org/10.21236/ada456062.

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Patro, S., und W. J. Kolarik. Integrated evolutionary computation neural network quality controller for automated systems. Office of Scientific and Technical Information (OSTI), Juni 1999. http://dx.doi.org/10.2172/350895.

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Leij, F. J., und M. T. Van Genuchten. Development of Pedotransfer Functions with Neural Network Models. Fort Belvoir, VA: Defense Technical Information Center, Juni 2001. http://dx.doi.org/10.21236/ada394563.

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Fox-Rabinovitz, M. S., und V. M. Krasnopolsky. Development of Ensemble Neural Network Convection Parameterizations for Climate Models. Office of Scientific and Technical Information (OSTI), Mai 2012. http://dx.doi.org/10.2172/1039344.

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Rajagopalan, A., G. Washington, G. Rizzoni und Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), Dezember 2003. http://dx.doi.org/10.2172/15006009.

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Raychev, Nikolay. Can human thoughts be encoded, decoded and manipulated to achieve symbiosis of the brain and the machine. Web of Open Science, Oktober 2020. http://dx.doi.org/10.37686/nsrl.v1i2.76.

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This article discusses the current state of neurointerface technologies, not limited to deep electrode approaches. There are new heuristic ideas for creating a fast and broadband channel from the brain to artificial intelligence. One of the ideas is not to decipher the natural codes of nerve cells, but to create conditions for the development of a new language for communication between the human brain and artificial intelligence tools. Theoretically, this is possible if the brain "feels" that by changing the activity of nerve cells that communicate with the computer, it is possible to "achieve" the necessary actions for the body in the external environment, for example, to take a cup of coffee or turn on your favorite music. At the same time, an artificial neural network that analyzes the flow of nerve impulses must also be directed at the brain, trying to guess the body's needs at the moment with a minimum number of movements. The most important obstacle to further progress is the problem of biocompatibility, which has not yet been resolved. This is even more important than the number of electrodes and the power of the processors on the chip. When you insert a foreign object into your brain, it tries to isolate itself from it. This is a multidisciplinary topic not only for doctors and psychophysiologists, but also for engineers, programmers, mathematicians. Of course, the problem is complex and it will be possible to overcome it only with joint efforts.
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